experiments/Hotel-v2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmlWL-oDQ3Tw"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ds4v/absa-vlsp-2018/blob/main/experiments/Hotel-v1.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSyJrDH1WIJg",
        "outputId": "ccdb20be-cc46-415a-cc10-85ae57101f85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m431.4/431.4 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for vncorenlp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m40.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m52.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q emoji\n",
        "!pip install -q vncorenlp\n",
        "!pip install -q datasets\n",
        "!pip install -q accelerate\n",
        "!pip install -q transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wa64OaV9WIJd",
        "outputId": "c57b9b23-9b7a-4915-a181-a39a127950da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'absa-vlsp-2018'...\n",
            "remote: Enumerating objects: 225, done.\u001b[K\n",
            "remote: Counting objects: 100% (136/136), done.\u001b[K\n",
            "remote: Compressing objects: 100% (96/96), done.\u001b[K\n",
            "remote: Total 225 (delta 61), reused 106 (delta 36), pack-reused 89\u001b[K\n",
            "Receiving objects: 100% (225/225), 31.72 MiB | 28.60 MiB/s, done.\n",
            "Resolving deltas: 100% (78/78), done.\n",
            "/content/absa-vlsp-2018/experiments\n",
            "Hotel-v1.ipynb\tHotel-v2.ipynb\tpredictions  Restaurant-v1.ipynb  Restaurant-v2.ipynb\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ds4v/absa-vlsp-2018.git\n",
        "%cd ./absa-vlsp-2018/experiments\n",
        "!mkdir predictions\n",
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ebq2fB17WIJh",
        "outputId": "6a3a9309-9fa4-4d7f-b53a-518a20fa7c14"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.15.0'"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import sys\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "sys.path.append('..')\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asXVePdEwpbc",
        "outputId": "4c1f3a3f-0e22-4fa0-c51f-e28073e0b2f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using GPU: /device:GPU:0\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n"
          ]
        }
      ],
      "source": [
        "if tf.config.list_physical_devices('GPU'):\n",
        "    physical_devices = tf.config.list_physical_devices('GPU')\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
        "    print('Using GPU:', tf.test.gpu_device_name())\n",
        "    !nvcc -V\n",
        "else: raise ValueError('Running on CPU is not recommended.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Le1NSd1ZWIJi"
      },
      "source": [
        "# Constants Setup and Load the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "8jfvHoZpWIJj"
      },
      "outputs": [],
      "source": [
        "TRAIN_PATH = r'../datasets/vlsp2018_hotel/1-VLSP2018-SA-Hotel-train.csv'\n",
        "VAL_PATH = r'../datasets/vlsp2018_hotel/2-VLSP2018-SA-Hotel-dev.csv'\n",
        "TEST_PATH = r'../datasets/vlsp2018_hotel/3-VLSP2018-SA-Hotel-test.csv'\n",
        "PRETRAINED_MODEL = 'vinai/phobert-base'\n",
        "MODEL_NAME = 'Hotel-v2' # Multitask model on Hotel domain\n",
        "MAX_LENGTH = 256\n",
        "BATCH_SIZE = 25\n",
        "EPOCHS = 20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564,
          "referenced_widgets": [
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            "1e50003dc84449e481ac7198bab1fbb8",
            "6d5b3d3b72614d88a84b23e5b9ab22bc"
          ]
        },
        "id": "MtJDqdUUWIJk",
        "outputId": "92f26818-05dc-48e6-c857-06968a43a72e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d3d9251a1534d11b86975b0dce04c95",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a0676a19c0394c07aeb01d89265d31bb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating val split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "431714d9c07042cab4b15222b45abadb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Review', 'FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL'],\n",
              "        num_rows: 3000\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['Review', 'FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Review', 'FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL'],\n",
              "        num_rows: 600\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from processors.vlsp2018_processor import VLSP2018Loader\n",
        "raw_datasets = VLSP2018Loader.load(TRAIN_PATH, VAL_PATH, TEST_PATH)\n",
        "raw_datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2nSyWXOvWIJl"
      },
      "source": [
        "# Preprocess and Tokenize the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348,
          "referenced_widgets": [
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          ]
        },
        "id": "43XpEFg9WIJl",
        "outputId": "294dfd64-bad7-4f16-b29a-24c2325df53c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "VnCoreNLP word segmenter is loaded successfully.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "model_id": "4434a66472594858a140fee38ee29914",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/961 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "3c4ac89377d14b1f9048c71b32e890ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.58G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c8e363f83490497b8ff7fe58a498e30e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "6b9f7cecc9ae48ada8b0fbb49ba4886b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "81175368e52d42d69a011c5222b1ae7e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "dict.txt:   0%|          | 0.00/360k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "60a51f1c88044f3dbdb7f106d5ead6b2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/279 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "bmd1905/vietnamese-correction-v2 is loaded successfully.\n"
          ]
        }
      ],
      "source": [
        "from processors.vietnamese_processor import VietnameseTextPreprocessor\n",
        "\n",
        "# You should be carefull when using single word replacement for acronyms, because it can cause misinterpretation.\n",
        "# For example, 'giá': ['price', 'gia'] can replace the word 'gia' in 'gia đình', making it become 'giá đình'.\n",
        "vn_preprocessor = VietnameseTextPreprocessor(vncorenlp_dir='../processors/VnCoreNLP', extra_teencodes={\n",
        "    'khách sạn': ['ks'], 'nhà hàng': ['nhahang'], 'nhân viên': ['nv'],\n",
        "    'cửa hàng': ['store', 'sop', 'shopE', 'shop'],\n",
        "    'sản phẩm': ['sp', 'product'], 'hàng': ['hàg'],\n",
        "    'giao hàng': ['ship', 'delivery', 'síp'], 'đặt hàng': ['order'],\n",
        "    'chuẩn chính hãng': ['authentic', 'aut', 'auth'], 'hạn sử dụng': ['date', 'hsd'],\n",
        "    'điện thoại': ['dt'],  'facebook': ['fb', 'face'],\n",
        "    'nhắn tin': ['nt', 'ib'], 'trả lời': ['tl', 'trl', 'rep'],\n",
        "    'feedback': ['fback', 'fedback'], 'sử dụng': ['sd'], 'xài': ['sài'],\n",
        "}, max_correction_length=MAX_LENGTH)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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          ]
        },
        "id": "sgtRu6oMWIJm",
        "outputId": "dc586198-05f5-4847-d3ce-3f79fe00e918"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ba0054911ee42f9b66049b604c970ac",
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              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/557 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8ecfc631ebc49239c32899b68552b5b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/895k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f02f6f449ce4524a9239d410ed3c2af",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "bpe.codes:   0%|          | 0.00/1.14M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ebdaad379bd4734a65b3257aaafccda",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/3.13M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Encode: [0, 218, 8, 649, 212, 956, 2413, 195, 5, 2] \n",
            "Decode: <s> Tôi là sinh_viên trường đại_học Công_nghệ thông_tin. </s>\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "['input_ids', 'token_type_ids', 'attention_mask']"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL)\n",
        "tokens = tokenizer.encode('Tôi là sinh_viên trường đại_học Công_nghệ thông_tin .')\n",
        "print('Encode:', tokens, '\\nDecode:', tokenizer.decode(tokens))\n",
        "tokenizer.model_input_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "b32O1vDvWIJm",
        "outputId": "b37fdf9b-d8ad-47ed-be20-6ee7a7d53bea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] Preprocessing and tokenizing text data...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2b91009cece0431bbdd6d814b0d12c81",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/3000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af366af79e9b4746ab02bead0e0a1cd0",
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            },
            "text/plain": [
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "963b8d2eff7348719509eb290e2a7932",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6ee77e1a27d4dcf9ad27cf551782c2e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/3000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6599fa5a724a4a378d6f0fb84e45ecd7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "044ff068980942baa2d95aefccf99085",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/600 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 3000\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 600\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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nh\\u00ecn th\\u1ea5y m\\u1ed9t kho\\u1ea3ng \\u0111\\u1ea5t r\\u1ea5t to ban b\\u00ean kia c\\u1eeda_s\\u1ed5 n\\u01b0\\u1edbc th\\u00ec ch\\u1ea3y r\\u1ea5t l\\u00e0 y\\u1ebfu b\\u1ed3n_c\\u1ea7u th\\u00ec b\\u1ea1n ph\\u1ea3i \\u0111\\u1ed5i r\\u1ea5t nhi\\u1ec1u l\\u1ea7n b\\u1ed3n_t\\u1eafm th\\u00ec nh\\u00ecn kh\\u00f4ng s\\u1ea1ch_s\\u1ebd ph\\u00f2ng th\\u00ec c\\u00f3 m\\u00f9i h\\u00f4i m\\u00e0 b\\u1eefa t\\u1ed1i h\\u00f4m \\u0111\\u00f3 t\\u1edbi kh\\u00e1ch_s\\u1ea1n r\\u1ea5t \\u1ed3n_\\u00e0o v\\u00e0 b\\u1eefa \\u0103n s\\u00e1ng th\\u00ec t\\u00f4i \\u0111i ra l\\u00e0 kho\\u1ea3ng 9h30 nh\\u00e2n_vi\\u00ean nh\\u00e0 h\\u00e0g n\\u00f3i l\\u00e0 gi\\u1edd \\u0103n s\\u00e1ng h\\u1ebft r\\u1ed3i gh\\u1ebf_ng\\u1ed3i nh\\u00ecn r\\u1ea5t b\\u1ea9n h\\u1ed3 b\\u01a1i nh\\u00ecn c\\u0169ng d\\u01a1 l\\u1eafm c\\u00f3 c\\u00e1i c\\u1ed1ng r\\u1ea5t l\\u00e0 h\\u00f4i kh\\u00f4ng ch\\u1ecbu \\u0111\\u01b0\\u1ee3c t\\u00f3m_l\\u1ea1i v\\u1eeba_qua t\\u00f4i c\\u00f3 m\\u1ed9t k\\u00ec ngh\\u1ec9 r\\u1ea5t l\\u00e0 t\\u1ec7 l\\u00e0m cho m\\u00ecnh c\\u00f3 c\\u1ea3m_gi\\u00e1c b\\u1ef1c_b\\u1ed9i kh\\u00f4ng ch\\u1ecbu \\u0111\\u01b0\\u1ee3c t\\u00f4i ngh\\u0129 khu ngh\\u1ec9_d\\u01b0\\u1ee1ng n\\u00e0y ch\\u1ec9 h\\u01a1n nh\\u00e0_tr\\u1ecd v\\u00e0 kh\\u00e1ch_s\\u1ea1n lo\\u1ea1i th\\u01b0\\u1eddng 1 ch\\u00fat_x\\u00edu l\\u00e0 c\\u00f3 b\\u1ed3n_t\\u1eafm ri\\u00eang th\\u00f4i </s> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": 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              "      <th></th>\n",
              "      <th>raw_datasets</th>\n",
              "      <th>encoded_input_ids</th>\n",
              "      <th>decoded_input_ids</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nhan viên phục vụ chu đáo Tuy chuyến du lịch c...</td>\n",
              "      <td>[0, 650, 533, 5655, 2442, 550, 388, 7, 70, 66,...</td>\n",
              "      <td>&lt;s&gt; nhân_viên phục_vụ chu_đáo tuy chuyến du_lị...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bãi biển đẹp - Phục vụ tốt- Thức ăn ngon, giá ...</td>\n",
              "      <td>[0, 1162, 262, 258, 533, 167, 1849, 1325, 3401...</td>\n",
              "      <td>&lt;s&gt; bãi biển đẹp phục_vụ tốt thức_ăn ngon giá_...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Phòng nằm gần biển đẹp Phục vụ phòng t...</td>\n",
              "      <td>[0, 292, 339, 124, 262, 258, 533, 292, 167, 58...</td>\n",
              "      <td>&lt;s&gt; phòng nằm gần biển đẹp phục_vụ phòng tốt d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Nhân viên chuyên nghiệp tuyệt Khách sạn nằm ở ...</td>\n",
              "      <td>[0, 650, 1564, 3518, 1299, 339, 25, 442, 9756,...</td>\n",
              "      <td>&lt;s&gt; nhân_viên chuyên_nghiệp tuyệt khách_sạn nằ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gia đình mình đã có 4 ngày 3 đêm lưu trú tại C...</td>\n",
              "      <td>[0, 161, 68, 14, 10, 163, 43, 107, 396, 6567, ...</td>\n",
              "      <td>&lt;s&gt; gia_đình mình đã có 4 ngày 3 đêm lưu_trú t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Tôi ở đây 3 ngày 2 đêm, khách sạn không đúng v...</td>\n",
              "      <td>[0, 70, 25, 97, 107, 43, 76, 396, 1299, 17, 28...</td>\n",
              "      <td>&lt;s&gt; tôi ở đây 3 ngày 2 đêm khách_sạn không đún...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Resort đẹp, Nhân viên phục vụ tận tình. Mình r...</td>\n",
              "      <td>[0, 9823, 258, 650, 533, 9787, 68, 59, 2273, 2...</td>\n",
              "      <td>&lt;s&gt; resort đẹp nhân_viên phục_vụ tận_tình mình...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Đây là lần đầu tiên chúng tôi đến Phú Quốc. Nh...</td>\n",
              "      <td>[0, 97, 8, 101, 191, 283, 30, 56407, 3705, 595...</td>\n",
              "      <td>&lt;s&gt; đây là lần đầu_tiên chúng_tôi đến phú_quốc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Tuy chuyến du lịch của tôi chỉ kéo dài có 3 ng...</td>\n",
              "      <td>[0, 2442, 550, 388, 7, 70, 66, 832, 10, 107, 4...</td>\n",
              "      <td>&lt;s&gt; tuy chuyến du_lịch của tôi chỉ kéo_dài có ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bình Minh Resort Cable Car Vũng Tàu (KDL Cáp T...</td>\n",
              "      <td>[0, 14872, 9823, 1395, 22820, 26283, 44439, 59...</td>\n",
              "      <td>&lt;s&gt; bình_minh resort cable car vũng_tàu khu cá...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f141cb7-877f-411d-98bf-ec204c79a250')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4f141cb7-877f-411d-98bf-ec204c79a250 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4f141cb7-877f-411d-98bf-ec204c79a250');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f0aa2f73-9fe8-413c-a2c2-086214374bb2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f0aa2f73-9fe8-413c-a2c2-086214374bb2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f0aa2f73-9fe8-413c-a2c2-086214374bb2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                        raw_datasets  \\\n",
              "0  Nhan viên phục vụ chu đáo Tuy chuyến du lịch c...   \n",
              "1  Bãi biển đẹp - Phục vụ tốt- Thức ăn ngon, giá ...   \n",
              "2  Phòng nằm gần biển đẹp Phục vụ phòng t...   \n",
              "3  Nhân viên chuyên nghiệp tuyệt Khách sạn nằm ở ...   \n",
              "4  Gia đình mình đã có 4 ngày 3 đêm lưu trú tại C...   \n",
              "5  Tôi ở đây 3 ngày 2 đêm, khách sạn không đúng v...   \n",
              "6  Resort đẹp, Nhân viên phục vụ tận tình. Mình r...   \n",
              "7  Đây là lần đầu tiên chúng tôi đến Phú Quốc. Nh...   \n",
              "8  Tuy chuyến du lịch của tôi chỉ kéo dài có 3 ng...   \n",
              "9  Bình Minh Resort Cable Car Vũng Tàu (KDL Cáp T...   \n",
              "\n",
              "                                   encoded_input_ids  \\\n",
              "0  [0, 650, 533, 5655, 2442, 550, 388, 7, 70, 66,...   \n",
              "1  [0, 1162, 262, 258, 533, 167, 1849, 1325, 3401...   \n",
              "2  [0, 292, 339, 124, 262, 258, 533, 292, 167, 58...   \n",
              "3  [0, 650, 1564, 3518, 1299, 339, 25, 442, 9756,...   \n",
              "4  [0, 161, 68, 14, 10, 163, 43, 107, 396, 6567, ...   \n",
              "5  [0, 70, 25, 97, 107, 43, 76, 396, 1299, 17, 28...   \n",
              "6  [0, 9823, 258, 650, 533, 9787, 68, 59, 2273, 2...   \n",
              "7  [0, 97, 8, 101, 191, 283, 30, 56407, 3705, 595...   \n",
              "8  [0, 2442, 550, 388, 7, 70, 66, 832, 10, 107, 4...   \n",
              "9  [0, 14872, 9823, 1395, 22820, 26283, 44439, 59...   \n",
              "\n",
              "                                   decoded_input_ids  \n",
              "0  <s> nhân_viên phục_vụ chu_đáo tuy chuyến du_lị...  \n",
              "1  <s> bãi biển đẹp phục_vụ tốt thức_ăn ngon giá_...  \n",
              "2  <s> phòng nằm gần biển đẹp phục_vụ phòng tốt d...  \n",
              "3  <s> nhân_viên chuyên_nghiệp tuyệt khách_sạn nằ...  \n",
              "4  <s> gia_đình mình đã có 4 ngày 3 đêm lưu_trú t...  \n",
              "5  <s> tôi ở đây 3 ngày 2 đêm khách_sạn không đún...  \n",
              "6  <s> resort đẹp nhân_viên phục_vụ tận_tình mình...  \n",
              "7  <s> đây là lần đầu_tiên chúng_tôi đến phú_quốc...  \n",
              "8  <s> tuy chuyến du_lịch của tôi chỉ kéo_dài có ...  \n",
              "9  <s> bình_minh resort cable car vũng_tàu khu cá...  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# preprocessed_datasets = VLSP2018Loader.preprocess_and_tokenize(raw_datasets, vn_preprocessor, tokenizer, BATCH_SIZE * 2, MAX_LENGTH)\n",
        "# preprocessed_datasets.save_to_disk('../datasets/preprocessed_hotel')\n",
        "# display(preprocessed_datasets)\n",
        "# pd.DataFrame({\n",
        "#     'raw_datasets': raw_datasets['train']['Review'][1480:1490],\n",
        "#     'encoded_input_ids': preprocessed_datasets['train']['input_ids'][1480:1490],\n",
        "#     'decoded_input_ids': [tokenizer.decode(preprocessed_datasets['train'][i]['input_ids']) for i in range(1480, 1490)]\n",
        "# })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSM6s-J4WIJn",
        "outputId": "f4ba00e0-6af6-4473-da56-c743bb69f42b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 3000\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['FACILITIES#CLEANLINESS', 'FACILITIES#COMFORT', 'FACILITIES#DESIGN&FEATURES', 'FACILITIES#GENERAL', 'FACILITIES#MISCELLANEOUS', 'FACILITIES#PRICES', 'FACILITIES#QUALITY', 'FOOD&DRINKS#MISCELLANEOUS', 'FOOD&DRINKS#PRICES', 'FOOD&DRINKS#QUALITY', 'FOOD&DRINKS#STYLE&OPTIONS', 'HOTEL#CLEANLINESS', 'HOTEL#COMFORT', 'HOTEL#DESIGN&FEATURES', 'HOTEL#GENERAL', 'HOTEL#MISCELLANEOUS', 'HOTEL#PRICES', 'HOTEL#QUALITY', 'LOCATION#GENERAL', 'ROOMS#CLEANLINESS', 'ROOMS#COMFORT', 'ROOMS#DESIGN&FEATURES', 'ROOMS#GENERAL', 'ROOMS#MISCELLANEOUS', 'ROOMS#PRICES', 'ROOMS#QUALITY', 'ROOM_AMENITIES#CLEANLINESS', 'ROOM_AMENITIES#COMFORT', 'ROOM_AMENITIES#DESIGN&FEATURES', 'ROOM_AMENITIES#GENERAL', 'ROOM_AMENITIES#MISCELLANEOUS', 'ROOM_AMENITIES#PRICES', 'ROOM_AMENITIES#QUALITY', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 600\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_from_disk\n",
        "preprocessed_datasets = load_from_disk('../datasets/preprocessed_hotel')\n",
        "preprocessed_datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGRUGqTWWIJn"
      },
      "source": [
        "# Prepare for TensorFlow Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqHvWQYkLtqi",
        "outputId": "2c378394-18e1-411e-d301-64c0526996bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'FACILITIES#CLEANLINESS': 'output_1', 'FACILITIES#COMFORT': 'output_2', 'FACILITIES#DESIGN&FEATURES': 'output_3', 'FACILITIES#GENERAL': 'output_4', 'FACILITIES#MISCELLANEOUS': 'output_5', 'FACILITIES#PRICES': 'output_6', 'FACILITIES#QUALITY': 'output_7', 'FOOD&DRINKS#MISCELLANEOUS': 'output_8', 'FOOD&DRINKS#PRICES': 'output_9', 'FOOD&DRINKS#QUALITY': 'output_10', 'FOOD&DRINKS#STYLE&OPTIONS': 'output_11', 'HOTEL#CLEANLINESS': 'output_12', 'HOTEL#COMFORT': 'output_13', 'HOTEL#DESIGN&FEATURES': 'output_14', 'HOTEL#GENERAL': 'output_15', 'HOTEL#MISCELLANEOUS': 'output_16', 'HOTEL#PRICES': 'output_17', 'HOTEL#QUALITY': 'output_18', 'LOCATION#GENERAL': 'output_19', 'ROOMS#CLEANLINESS': 'output_20', 'ROOMS#COMFORT': 'output_21', 'ROOMS#DESIGN&FEATURES': 'output_22', 'ROOMS#GENERAL': 'output_23', 'ROOMS#MISCELLANEOUS': 'output_24', 'ROOMS#PRICES': 'output_25', 'ROOMS#QUALITY': 'output_26', 'ROOM_AMENITIES#CLEANLINESS': 'output_27', 'ROOM_AMENITIES#COMFORT': 'output_28', 'ROOM_AMENITIES#DESIGN&FEATURES': 'output_29', 'ROOM_AMENITIES#GENERAL': 'output_30', 'ROOM_AMENITIES#MISCELLANEOUS': 'output_31', 'ROOM_AMENITIES#PRICES': 'output_32', 'ROOM_AMENITIES#QUALITY': 'output_33', 'SERVICE#GENERAL': 'output_34'}\n"
          ]
        }
      ],
      "source": [
        "# Tensorflow only accepts targeted column names prefixed with 'output_'\n",
        "MULTI_BRANCH_NAMES = {old_name: f'output_{i + 1}' for i, old_name in enumerate(raw_datasets['train'].column_names[1:])}\n",
        "preprocessed_datasets = preprocessed_datasets.rename_columns(MULTI_BRANCH_NAMES)\n",
        "steps_per_epoch = len(preprocessed_datasets['train']) // BATCH_SIZE\n",
        "total_steps = EPOCHS * steps_per_epoch\n",
        "print(MULTI_BRANCH_NAMES)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "AWlh8K7OWIJn"
      },
      "outputs": [],
      "source": [
        "train_tf_dataset = preprocessed_datasets['train'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols=list(MULTI_BRANCH_NAMES.values()),\n",
        "    batch_size=BATCH_SIZE, shuffle=True, num_workers=8\n",
        ")\n",
        "val_tf_dataset = preprocessed_datasets['val'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols=list(MULTI_BRANCH_NAMES.values()),\n",
        "    batch_size=BATCH_SIZE, shuffle=False, num_workers=8\n",
        ")\n",
        "test_tf_dataset = preprocessed_datasets['test'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols=list(MULTI_BRANCH_NAMES.values()),\n",
        "    batch_size=BATCH_SIZE, shuffle=False, num_workers=8\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "cxdzfzzdZB5T"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.optimizers.schedules import CosineDecay\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "optimizer = Adam(learning_rate=CosineDecay(\n",
        "    initial_learning_rate = 1e-4,\n",
        "    warmup_target = 2e-4,\n",
        "    warmup_steps = int(total_steps * 0.15), # 15% of total_steps\n",
        "    decay_steps = int(total_steps * 0.3), # Next 30% of total_steps\n",
        "    alpha = 0.1, # Minimum lr for decay as a fraction of initial_learning_rate\n",
        "))\n",
        "\n",
        "early_stop_callback = EarlyStopping(\n",
        "    monitor = 'val_loss',\n",
        "    patience = 3, # Stop if no improvement after 3 epochs\n",
        "    restore_best_weights = True, # You may obtain HIGHER metrics on the test set with longer training time if you set this to False\n",
        "    # Because after some experiments, I found that even with higher val_loss, it still results in better metric reports on the test set.\n",
        "    # This maybe because the training set and the test set have more similarities than the validation data.\n",
        "    # But I think this is not fair, as we already have prior knowledge about the test set and we modified our training based on the performance on this set.\n",
        "    # In real-world, we should only modify our training based on the performance on the validation data\n",
        "    verbose = 1\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zF3Un44v3cNy"
      },
      "source": [
        "# Fine-tuning with TensorFlow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ots6QvfKNS9E",
        "outputId": "31d772b7-a9c2-413d-e0f7-05a1c36defea"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at vinai/phobert-base were not used when initializing TFRobertaModel: ['lm_head']\n",
            "- This IS expected if you are initializing TFRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFRobertaModel were initialized from the model checkpoint at vinai/phobert-base.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model_5/roberta/pooler/dense/kernel:0', 'tf_roberta_model_5/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model_5/roberta/pooler/dense/kernel:0', 'tf_roberta_model_5/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model_5/roberta/pooler/dense/kernel:0', 'tf_roberta_model_5/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model_5/roberta/pooler/dense/kernel:0', 'tf_roberta_model_5/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "120/120 [==============================] - 133s 576ms/step - loss: 13.4663 - output_1_loss: 0.2622 - output_2_loss: 0.2421 - output_3_loss: 0.5772 - output_4_loss: 0.3292 - output_5_loss: 0.0964 - output_6_loss: 0.1431 - output_7_loss: 0.3277 - output_8_loss: 0.0637 - output_9_loss: 0.2149 - output_10_loss: 0.6547 - output_11_loss: 0.5773 - output_12_loss: 0.4296 - output_13_loss: 0.7129 - output_14_loss: 0.7562 - output_15_loss: 0.8814 - output_16_loss: 0.2070 - output_17_loss: 0.5635 - output_18_loss: 0.3564 - output_19_loss: 0.7928 - output_20_loss: 0.5936 - output_21_loss: 0.4946 - output_22_loss: 0.7854 - output_23_loss: 0.3722 - output_24_loss: 0.0459 - output_25_loss: 0.3210 - output_26_loss: 0.2932 - output_27_loss: 0.1824 - output_28_loss: 0.1995 - output_29_loss: 0.4302 - output_30_loss: 0.3110 - output_31_loss: 0.0521 - output_32_loss: 0.0353 - output_33_loss: 0.3243 - output_34_loss: 0.8372 - val_loss: 9.2001 - val_output_1_loss: 0.0830 - val_output_2_loss: 0.1629 - val_output_3_loss: 0.3924 - val_output_4_loss: 0.1419 - val_output_5_loss: 0.0276 - val_output_6_loss: 0.0996 - val_output_7_loss: 0.2491 - val_output_8_loss: 0.0383 - val_output_9_loss: 0.0836 - val_output_10_loss: 0.3712 - val_output_11_loss: 0.3959 - val_output_12_loss: 0.3171 - val_output_13_loss: 0.5227 - val_output_14_loss: 0.4740 - val_output_15_loss: 0.4852 - val_output_16_loss: 0.3352 - val_output_17_loss: 0.2800 - val_output_18_loss: 0.1339 - val_output_19_loss: 0.3719 - val_output_20_loss: 0.3651 - val_output_21_loss: 0.4183 - val_output_22_loss: 0.5404 - val_output_23_loss: 0.1995 - val_output_24_loss: 0.0220 - val_output_25_loss: 0.1302 - val_output_26_loss: 0.0633 - val_output_27_loss: 0.2463 - val_output_28_loss: 0.5257 - val_output_29_loss: 0.7167 - val_output_30_loss: 0.1535 - val_output_31_loss: 0.0164 - val_output_32_loss: 0.0251 - val_output_33_loss: 0.2469 - val_output_34_loss: 0.5649\n",
            "Epoch 2/20\n",
            "120/120 [==============================] - 60s 455ms/step - loss: 9.1108 - output_1_loss: 0.1760 - output_2_loss: 0.1831 - output_3_loss: 0.4276 - output_4_loss: 0.2735 - output_5_loss: 0.0606 - output_6_loss: 0.0977 - output_7_loss: 0.2577 - output_8_loss: 0.0308 - output_9_loss: 0.1522 - output_10_loss: 0.3824 - output_11_loss: 0.4004 - output_12_loss: 0.2582 - output_13_loss: 0.5013 - output_14_loss: 0.5412 - output_15_loss: 0.6948 - output_16_loss: 0.1756 - output_17_loss: 0.3067 - output_18_loss: 0.2843 - output_19_loss: 0.4348 - output_20_loss: 0.3050 - output_21_loss: 0.3618 - output_22_loss: 0.5334 - output_23_loss: 0.3076 - output_24_loss: 0.0143 - output_25_loss: 0.2235 - output_26_loss: 0.2088 - output_27_loss: 0.1179 - output_28_loss: 0.1474 - output_29_loss: 0.3232 - output_30_loss: 0.2239 - output_31_loss: 0.0102 - output_32_loss: 0.0028 - output_33_loss: 0.2476 - output_34_loss: 0.4445 - val_loss: 7.2220 - val_output_1_loss: 0.0573 - val_output_2_loss: 0.1577 - val_output_3_loss: 0.3471 - val_output_4_loss: 0.1773 - val_output_5_loss: 0.0260 - val_output_6_loss: 0.0867 - val_output_7_loss: 0.2189 - val_output_8_loss: 0.0363 - val_output_9_loss: 0.0810 - val_output_10_loss: 0.2701 - val_output_11_loss: 0.2968 - val_output_12_loss: 0.1876 - val_output_13_loss: 0.5032 - val_output_14_loss: 0.3555 - val_output_15_loss: 0.3243 - val_output_16_loss: 0.3274 - val_output_17_loss: 0.1673 - val_output_18_loss: 0.0944 - val_output_19_loss: 0.2649 - val_output_20_loss: 0.1986 - val_output_21_loss: 0.3597 - val_output_22_loss: 0.4545 - val_output_23_loss: 0.1898 - val_output_24_loss: 0.0205 - val_output_25_loss: 0.1051 - val_output_26_loss: 0.0324 - val_output_27_loss: 0.2160 - val_output_28_loss: 0.3590 - val_output_29_loss: 0.5552 - val_output_30_loss: 0.1172 - val_output_31_loss: 0.0137 - val_output_32_loss: 0.0245 - val_output_33_loss: 0.2246 - val_output_34_loss: 0.3715\n",
            "Epoch 3/20\n",
            "120/120 [==============================] - 59s 451ms/step - loss: 6.7128 - output_1_loss: 0.1227 - output_2_loss: 0.1606 - output_3_loss: 0.3412 - output_4_loss: 0.2458 - output_5_loss: 0.0529 - output_6_loss: 0.0882 - output_7_loss: 0.1935 - output_8_loss: 0.0285 - output_9_loss: 0.1246 - output_10_loss: 0.2612 - output_11_loss: 0.2851 - output_12_loss: 0.1438 - output_13_loss: 0.3620 - output_14_loss: 0.3616 - output_15_loss: 0.4793 - output_16_loss: 0.1595 - output_17_loss: 0.2092 - output_18_loss: 0.2011 - output_19_loss: 0.2903 - output_20_loss: 0.1756 - output_21_loss: 0.2847 - output_22_loss: 0.3535 - output_23_loss: 0.2744 - output_24_loss: 0.0125 - output_25_loss: 0.1767 - output_26_loss: 0.1612 - output_27_loss: 0.1037 - output_28_loss: 0.1187 - output_29_loss: 0.2601 - output_30_loss: 0.1713 - output_31_loss: 0.0094 - output_32_loss: 0.0018 - output_33_loss: 0.1997 - output_34_loss: 0.2983 - val_loss: 6.0657 - val_output_1_loss: 0.0525 - val_output_2_loss: 0.1489 - val_output_3_loss: 0.3023 - val_output_4_loss: 0.1493 - val_output_5_loss: 0.0292 - val_output_6_loss: 0.0716 - val_output_7_loss: 0.1603 - val_output_8_loss: 0.0352 - val_output_9_loss: 0.0704 - val_output_10_loss: 0.1824 - val_output_11_loss: 0.2133 - val_output_12_loss: 0.1223 - val_output_13_loss: 0.4454 - val_output_14_loss: 0.2997 - val_output_15_loss: 0.2822 - val_output_16_loss: 0.3076 - val_output_17_loss: 0.1314 - val_output_18_loss: 0.0808 - val_output_19_loss: 0.1992 - val_output_20_loss: 0.1769 - val_output_21_loss: 0.3125 - val_output_22_loss: 0.3550 - val_output_23_loss: 0.1497 - val_output_24_loss: 0.0228 - val_output_25_loss: 0.0784 - val_output_26_loss: 0.0417 - val_output_27_loss: 0.2044 - val_output_28_loss: 0.3321 - val_output_29_loss: 0.4723 - val_output_30_loss: 0.0954 - val_output_31_loss: 0.0140 - val_output_32_loss: 0.0286 - val_output_33_loss: 0.1930 - val_output_34_loss: 0.3048\n",
            "Epoch 4/20\n",
            "120/120 [==============================] - 59s 455ms/step - loss: 4.9274 - output_1_loss: 0.0856 - output_2_loss: 0.1462 - output_3_loss: 0.2630 - output_4_loss: 0.1924 - output_5_loss: 0.0460 - output_6_loss: 0.0713 - output_7_loss: 0.1556 - output_8_loss: 0.0248 - output_9_loss: 0.0945 - output_10_loss: 0.1733 - output_11_loss: 0.2100 - output_12_loss: 0.0889 - output_13_loss: 0.2633 - output_14_loss: 0.2503 - output_15_loss: 0.3395 - output_16_loss: 0.1340 - output_17_loss: 0.1552 - output_18_loss: 0.1506 - output_19_loss: 0.1879 - output_20_loss: 0.1090 - output_21_loss: 0.1994 - output_22_loss: 0.2366 - output_23_loss: 0.2003 - output_24_loss: 0.0121 - output_25_loss: 0.1216 - output_26_loss: 0.1276 - output_27_loss: 0.0857 - output_28_loss: 0.0972 - output_29_loss: 0.2129 - output_30_loss: 0.1215 - output_31_loss: 0.0081 - output_32_loss: 8.9158e-04 - output_33_loss: 0.1641 - output_34_loss: 0.1983 - val_loss: 5.4538 - val_output_1_loss: 0.0407 - val_output_2_loss: 0.1395 - val_output_3_loss: 0.3033 - val_output_4_loss: 0.1043 - val_output_5_loss: 0.0234 - val_output_6_loss: 0.0743 - val_output_7_loss: 0.1302 - val_output_8_loss: 0.0349 - val_output_9_loss: 0.0662 - val_output_10_loss: 0.1373 - val_output_11_loss: 0.1922 - val_output_12_loss: 0.0941 - val_output_13_loss: 0.4178 - val_output_14_loss: 0.2889 - val_output_15_loss: 0.3133 - val_output_16_loss: 0.2663 - val_output_17_loss: 0.1390 - val_output_18_loss: 0.0684 - val_output_19_loss: 0.1907 - val_output_20_loss: 0.1795 - val_output_21_loss: 0.2778 - val_output_22_loss: 0.3115 - val_output_23_loss: 0.1487 - val_output_24_loss: 0.0216 - val_output_25_loss: 0.0634 - val_output_26_loss: 0.0206 - val_output_27_loss: 0.1612 - val_output_28_loss: 0.2565 - val_output_29_loss: 0.4209 - val_output_30_loss: 0.0943 - val_output_31_loss: 0.0145 - val_output_32_loss: 0.0280 - val_output_33_loss: 0.1792 - val_output_34_loss: 0.2511\n",
            "Epoch 5/20\n",
            "120/120 [==============================] - 59s 447ms/step - loss: 3.3606 - output_1_loss: 0.0535 - output_2_loss: 0.1136 - output_3_loss: 0.1913 - output_4_loss: 0.1528 - output_5_loss: 0.0332 - output_6_loss: 0.0561 - output_7_loss: 0.1147 - output_8_loss: 0.0223 - output_9_loss: 0.0705 - output_10_loss: 0.1127 - output_11_loss: 0.1235 - output_12_loss: 0.0709 - output_13_loss: 0.1675 - output_14_loss: 0.1759 - output_15_loss: 0.2014 - output_16_loss: 0.0951 - output_17_loss: 0.0991 - output_18_loss: 0.1040 - output_19_loss: 0.1123 - output_20_loss: 0.0785 - output_21_loss: 0.1330 - output_22_loss: 0.1482 - output_23_loss: 0.1293 - output_24_loss: 0.0078 - output_25_loss: 0.0833 - output_26_loss: 0.0922 - output_27_loss: 0.0620 - output_28_loss: 0.0768 - output_29_loss: 0.1435 - output_30_loss: 0.0918 - output_31_loss: 0.0056 - output_32_loss: 6.2240e-04 - output_33_loss: 0.1216 - output_34_loss: 0.1157 - val_loss: 5.2679 - val_output_1_loss: 0.0493 - val_output_2_loss: 0.1330 - val_output_3_loss: 0.2712 - val_output_4_loss: 0.1003 - val_output_5_loss: 0.0231 - val_output_6_loss: 0.0639 - val_output_7_loss: 0.1195 - val_output_8_loss: 0.0312 - val_output_9_loss: 0.0663 - val_output_10_loss: 0.1474 - val_output_11_loss: 0.1685 - val_output_12_loss: 0.1195 - val_output_13_loss: 0.5210 - val_output_14_loss: 0.3227 - val_output_15_loss: 0.2463 - val_output_16_loss: 0.2701 - val_output_17_loss: 0.1160 - val_output_18_loss: 0.0632 - val_output_19_loss: 0.1426 - val_output_20_loss: 0.1368 - val_output_21_loss: 0.2312 - val_output_22_loss: 0.2687 - val_output_23_loss: 0.1228 - val_output_24_loss: 0.0210 - val_output_25_loss: 0.0650 - val_output_26_loss: 0.0324 - val_output_27_loss: 0.1434 - val_output_28_loss: 0.2876 - val_output_29_loss: 0.4258 - val_output_30_loss: 0.0789 - val_output_31_loss: 0.0133 - val_output_32_loss: 0.0278 - val_output_33_loss: 0.1917 - val_output_34_loss: 0.2465\n",
            "Epoch 6/20\n",
            "120/120 [==============================] - 58s 449ms/step - loss: 1.9544 - output_1_loss: 0.0305 - output_2_loss: 0.0883 - output_3_loss: 0.1123 - output_4_loss: 0.0885 - output_5_loss: 0.0254 - output_6_loss: 0.0378 - output_7_loss: 0.0686 - output_8_loss: 0.0145 - output_9_loss: 0.0399 - output_10_loss: 0.0635 - output_11_loss: 0.0758 - output_12_loss: 0.0367 - output_13_loss: 0.0825 - output_14_loss: 0.1008 - output_15_loss: 0.0947 - output_16_loss: 0.0643 - output_17_loss: 0.0571 - output_18_loss: 0.0507 - output_19_loss: 0.0610 - output_20_loss: 0.0377 - output_21_loss: 0.0697 - output_22_loss: 0.0793 - output_23_loss: 0.0696 - output_24_loss: 0.0060 - output_25_loss: 0.0484 - output_26_loss: 0.0553 - output_27_loss: 0.0373 - output_28_loss: 0.0542 - output_29_loss: 0.0942 - output_30_loss: 0.0601 - output_31_loss: 0.0048 - output_32_loss: 5.1687e-04 - output_33_loss: 0.0771 - output_34_loss: 0.0673 - val_loss: 5.0372 - val_output_1_loss: 0.0314 - val_output_2_loss: 0.1473 - val_output_3_loss: 0.2844 - val_output_4_loss: 0.1052 - val_output_5_loss: 0.0236 - val_output_6_loss: 0.0638 - val_output_7_loss: 0.1039 - val_output_8_loss: 0.0328 - val_output_9_loss: 0.0583 - val_output_10_loss: 0.1268 - val_output_11_loss: 0.1955 - val_output_12_loss: 0.0935 - val_output_13_loss: 0.4477 - val_output_14_loss: 0.2785 - val_output_15_loss: 0.2383 - val_output_16_loss: 0.2903 - val_output_17_loss: 0.1035 - val_output_18_loss: 0.0569 - val_output_19_loss: 0.1601 - val_output_20_loss: 0.1287 - val_output_21_loss: 0.2282 - val_output_22_loss: 0.2590 - val_output_23_loss: 0.1360 - val_output_24_loss: 0.0202 - val_output_25_loss: 0.0767 - val_output_26_loss: 0.0179 - val_output_27_loss: 0.1272 - val_output_28_loss: 0.2187 - val_output_29_loss: 0.4137 - val_output_30_loss: 0.0651 - val_output_31_loss: 0.0132 - val_output_32_loss: 0.0288 - val_output_33_loss: 0.2141 - val_output_34_loss: 0.2478\n",
            "Epoch 7/20\n",
            "120/120 [==============================] - 58s 447ms/step - loss: 1.0225 - output_1_loss: 0.0161 - output_2_loss: 0.0429 - output_3_loss: 0.0509 - output_4_loss: 0.0447 - output_5_loss: 0.0142 - output_6_loss: 0.0190 - output_7_loss: 0.0321 - output_8_loss: 0.0093 - output_9_loss: 0.0212 - output_10_loss: 0.0354 - output_11_loss: 0.0365 - output_12_loss: 0.0186 - output_13_loss: 0.0414 - output_14_loss: 0.0469 - output_15_loss: 0.0480 - output_16_loss: 0.0348 - output_17_loss: 0.0309 - output_18_loss: 0.0251 - output_19_loss: 0.0296 - output_20_loss: 0.0216 - output_21_loss: 0.0370 - output_22_loss: 0.0456 - output_23_loss: 0.0394 - output_24_loss: 0.0033 - output_25_loss: 0.0287 - output_26_loss: 0.0318 - output_27_loss: 0.0217 - output_28_loss: 0.0313 - output_29_loss: 0.0532 - output_30_loss: 0.0309 - output_31_loss: 0.0025 - output_32_loss: 3.8716e-04 - output_33_loss: 0.0405 - output_34_loss: 0.0369 - val_loss: 4.9877 - val_output_1_loss: 0.0309 - val_output_2_loss: 0.1464 - val_output_3_loss: 0.2861 - val_output_4_loss: 0.0931 - val_output_5_loss: 0.0244 - val_output_6_loss: 0.0667 - val_output_7_loss: 0.1118 - val_output_8_loss: 0.0329 - val_output_9_loss: 0.0642 - val_output_10_loss: 0.1207 - val_output_11_loss: 0.1690 - val_output_12_loss: 0.0942 - val_output_13_loss: 0.4774 - val_output_14_loss: 0.2733 - val_output_15_loss: 0.2409 - val_output_16_loss: 0.3097 - val_output_17_loss: 0.1137 - val_output_18_loss: 0.0581 - val_output_19_loss: 0.1385 - val_output_20_loss: 0.1384 - val_output_21_loss: 0.2307 - val_output_22_loss: 0.2509 - val_output_23_loss: 0.1345 - val_output_24_loss: 0.0222 - val_output_25_loss: 0.0682 - val_output_26_loss: 0.0136 - val_output_27_loss: 0.1433 - val_output_28_loss: 0.2421 - val_output_29_loss: 0.3785 - val_output_30_loss: 0.0720 - val_output_31_loss: 0.0138 - val_output_32_loss: 0.0304 - val_output_33_loss: 0.1747 - val_output_34_loss: 0.2223\n",
            "Epoch 8/20\n",
            "120/120 [==============================] - 59s 446ms/step - loss: 0.5623 - output_1_loss: 0.0094 - output_2_loss: 0.0221 - output_3_loss: 0.0265 - output_4_loss: 0.0203 - output_5_loss: 0.0063 - output_6_loss: 0.0127 - output_7_loss: 0.0183 - output_8_loss: 0.0057 - output_9_loss: 0.0106 - output_10_loss: 0.0195 - output_11_loss: 0.0211 - output_12_loss: 0.0119 - output_13_loss: 0.0229 - output_14_loss: 0.0238 - output_15_loss: 0.0264 - output_16_loss: 0.0189 - output_17_loss: 0.0176 - output_18_loss: 0.0147 - output_19_loss: 0.0188 - output_20_loss: 0.0122 - output_21_loss: 0.0208 - output_22_loss: 0.0258 - output_23_loss: 0.0201 - output_24_loss: 0.0027 - output_25_loss: 0.0155 - output_26_loss: 0.0181 - output_27_loss: 0.0118 - output_28_loss: 0.0194 - output_29_loss: 0.0295 - output_30_loss: 0.0163 - output_31_loss: 0.0019 - output_32_loss: 3.2200e-04 - output_33_loss: 0.0185 - output_34_loss: 0.0222 - val_loss: 4.9794 - val_output_1_loss: 0.0324 - val_output_2_loss: 0.1587 - val_output_3_loss: 0.3067 - val_output_4_loss: 0.0956 - val_output_5_loss: 0.0250 - val_output_6_loss: 0.0721 - val_output_7_loss: 0.1041 - val_output_8_loss: 0.0337 - val_output_9_loss: 0.0623 - val_output_10_loss: 0.1199 - val_output_11_loss: 0.1733 - val_output_12_loss: 0.0910 - val_output_13_loss: 0.4497 - val_output_14_loss: 0.2728 - val_output_15_loss: 0.2372 - val_output_16_loss: 0.3162 - val_output_17_loss: 0.1048 - val_output_18_loss: 0.0591 - val_output_19_loss: 0.1324 - val_output_20_loss: 0.1397 - val_output_21_loss: 0.2257 - val_output_22_loss: 0.2394 - val_output_23_loss: 0.1298 - val_output_24_loss: 0.0223 - val_output_25_loss: 0.0663 - val_output_26_loss: 0.0142 - val_output_27_loss: 0.1331 - val_output_28_loss: 0.2452 - val_output_29_loss: 0.3967 - val_output_30_loss: 0.0661 - val_output_31_loss: 0.0138 - val_output_32_loss: 0.0300 - val_output_33_loss: 0.1854 - val_output_34_loss: 0.2245\n",
            "Epoch 9/20\n",
            "120/120 [==============================] - 58s 446ms/step - loss: 0.4039 - output_1_loss: 0.0062 - output_2_loss: 0.0136 - output_3_loss: 0.0197 - output_4_loss: 0.0132 - output_5_loss: 0.0052 - output_6_loss: 0.0085 - output_7_loss: 0.0119 - output_8_loss: 0.0038 - output_9_loss: 0.0080 - output_10_loss: 0.0127 - output_11_loss: 0.0138 - output_12_loss: 0.0091 - output_13_loss: 0.0164 - output_14_loss: 0.0178 - output_15_loss: 0.0194 - output_16_loss: 0.0130 - output_17_loss: 0.0124 - output_18_loss: 0.0107 - output_19_loss: 0.0145 - output_20_loss: 0.0102 - output_21_loss: 0.0151 - output_22_loss: 0.0200 - output_23_loss: 0.0155 - output_24_loss: 0.0017 - output_25_loss: 0.0118 - output_26_loss: 0.0124 - output_27_loss: 0.0083 - output_28_loss: 0.0128 - output_29_loss: 0.0211 - output_30_loss: 0.0129 - output_31_loss: 0.0013 - output_32_loss: 2.8967e-04 - output_33_loss: 0.0131 - output_34_loss: 0.0174 - val_loss: 5.0051 - val_output_1_loss: 0.0318 - val_output_2_loss: 0.1582 - val_output_3_loss: 0.3041 - val_output_4_loss: 0.0969 - val_output_5_loss: 0.0258 - val_output_6_loss: 0.0702 - val_output_7_loss: 0.1069 - val_output_8_loss: 0.0345 - val_output_9_loss: 0.0621 - val_output_10_loss: 0.1188 - val_output_11_loss: 0.1731 - val_output_12_loss: 0.0945 - val_output_13_loss: 0.4632 - val_output_14_loss: 0.2768 - val_output_15_loss: 0.2333 - val_output_16_loss: 0.3285 - val_output_17_loss: 0.1061 - val_output_18_loss: 0.0587 - val_output_19_loss: 0.1330 - val_output_20_loss: 0.1385 - val_output_21_loss: 0.2265 - val_output_22_loss: 0.2430 - val_output_23_loss: 0.1306 - val_output_24_loss: 0.0226 - val_output_25_loss: 0.0669 - val_output_26_loss: 0.0146 - val_output_27_loss: 0.1332 - val_output_28_loss: 0.2358 - val_output_29_loss: 0.3898 - val_output_30_loss: 0.0663 - val_output_31_loss: 0.0138 - val_output_32_loss: 0.0300 - val_output_33_loss: 0.1889 - val_output_34_loss: 0.2282\n",
            "Epoch 10/20\n",
            "120/120 [==============================] - 58s 442ms/step - loss: 0.3553 - output_1_loss: 0.0057 - output_2_loss: 0.0114 - output_3_loss: 0.0166 - output_4_loss: 0.0126 - output_5_loss: 0.0044 - output_6_loss: 0.0070 - output_7_loss: 0.0103 - output_8_loss: 0.0037 - output_9_loss: 0.0067 - output_10_loss: 0.0110 - output_11_loss: 0.0124 - output_12_loss: 0.0082 - output_13_loss: 0.0151 - output_14_loss: 0.0158 - output_15_loss: 0.0180 - output_16_loss: 0.0096 - output_17_loss: 0.0114 - output_18_loss: 0.0096 - output_19_loss: 0.0133 - output_20_loss: 0.0092 - output_21_loss: 0.0134 - output_22_loss: 0.0185 - output_23_loss: 0.0133 - output_24_loss: 0.0014 - output_25_loss: 0.0101 - output_26_loss: 0.0113 - output_27_loss: 0.0070 - output_28_loss: 0.0118 - output_29_loss: 0.0171 - output_30_loss: 0.0119 - output_31_loss: 0.0011 - output_32_loss: 2.4615e-04 - output_33_loss: 0.0114 - output_34_loss: 0.0150 - val_loss: 5.0723 - val_output_1_loss: 0.0319 - val_output_2_loss: 0.1619 - val_output_3_loss: 0.3053 - val_output_4_loss: 0.0986 - val_output_5_loss: 0.0264 - val_output_6_loss: 0.0709 - val_output_7_loss: 0.1073 - val_output_8_loss: 0.0351 - val_output_9_loss: 0.0623 - val_output_10_loss: 0.1190 - val_output_11_loss: 0.1742 - val_output_12_loss: 0.0965 - val_output_13_loss: 0.4537 - val_output_14_loss: 0.2772 - val_output_15_loss: 0.2381 - val_output_16_loss: 0.3514 - val_output_17_loss: 0.1058 - val_output_18_loss: 0.0587 - val_output_19_loss: 0.1333 - val_output_20_loss: 0.1393 - val_output_21_loss: 0.2285 - val_output_22_loss: 0.2456 - val_output_23_loss: 0.1319 - val_output_24_loss: 0.0230 - val_output_25_loss: 0.0673 - val_output_26_loss: 0.0145 - val_output_27_loss: 0.1369 - val_output_28_loss: 0.2456 - val_output_29_loss: 0.3928 - val_output_30_loss: 0.0677 - val_output_31_loss: 0.0141 - val_output_32_loss: 0.0303 - val_output_33_loss: 0.1954 - val_output_34_loss: 0.2320\n",
            "Epoch 11/20\n",
            "120/120 [==============================] - ETA: 0s - loss: 0.3271 - output_1_loss: 0.0052 - output_2_loss: 0.0103 - output_3_loss: 0.0149 - output_4_loss: 0.0114 - output_5_loss: 0.0041 - output_6_loss: 0.0067 - output_7_loss: 0.0093 - output_8_loss: 0.0030 - output_9_loss: 0.0065 - output_10_loss: 0.0107 - output_11_loss: 0.0113 - output_12_loss: 0.0074 - output_13_loss: 0.0136 - output_14_loss: 0.0139 - output_15_loss: 0.0161 - output_16_loss: 0.0084 - output_17_loss: 0.0101 - output_18_loss: 0.0086 - output_19_loss: 0.0126 - output_20_loss: 0.0088 - output_21_loss: 0.0126 - output_22_loss: 0.0172 - output_23_loss: 0.0134 - output_24_loss: 0.0013 - output_25_loss: 0.0095 - output_26_loss: 0.0110 - output_27_loss: 0.0061 - output_28_loss: 0.0111 - output_29_loss: 0.0154 - output_30_loss: 0.0111 - output_31_loss: 0.0011 - output_32_loss: 2.3588e-04 - output_33_loss: 0.0104 - output_34_loss: 0.0137Restoring model weights from the end of the best epoch: 8.\n",
            "120/120 [==============================] - 59s 449ms/step - loss: 0.3271 - output_1_loss: 0.0052 - output_2_loss: 0.0103 - output_3_loss: 0.0149 - output_4_loss: 0.0114 - output_5_loss: 0.0041 - output_6_loss: 0.0067 - output_7_loss: 0.0093 - output_8_loss: 0.0030 - output_9_loss: 0.0065 - output_10_loss: 0.0107 - output_11_loss: 0.0113 - output_12_loss: 0.0074 - output_13_loss: 0.0136 - output_14_loss: 0.0139 - output_15_loss: 0.0161 - output_16_loss: 0.0084 - output_17_loss: 0.0101 - output_18_loss: 0.0086 - output_19_loss: 0.0126 - output_20_loss: 0.0088 - output_21_loss: 0.0126 - output_22_loss: 0.0172 - output_23_loss: 0.0134 - output_24_loss: 0.0013 - output_25_loss: 0.0095 - output_26_loss: 0.0110 - output_27_loss: 0.0061 - output_28_loss: 0.0111 - output_29_loss: 0.0154 - output_30_loss: 0.0111 - output_31_loss: 0.0011 - output_32_loss: 2.3588e-04 - output_33_loss: 0.0104 - output_34_loss: 0.0137 - val_loss: 5.1228 - val_output_1_loss: 0.0318 - val_output_2_loss: 0.1653 - val_output_3_loss: 0.3123 - val_output_4_loss: 0.0985 - val_output_5_loss: 0.0265 - val_output_6_loss: 0.0716 - val_output_7_loss: 0.1074 - val_output_8_loss: 0.0358 - val_output_9_loss: 0.0633 - val_output_10_loss: 0.1190 - val_output_11_loss: 0.1741 - val_output_12_loss: 0.0992 - val_output_13_loss: 0.4697 - val_output_14_loss: 0.2808 - val_output_15_loss: 0.2387 - val_output_16_loss: 0.3480 - val_output_17_loss: 0.1069 - val_output_18_loss: 0.0597 - val_output_19_loss: 0.1339 - val_output_20_loss: 0.1395 - val_output_21_loss: 0.2303 - val_output_22_loss: 0.2482 - val_output_23_loss: 0.1320 - val_output_24_loss: 0.0230 - val_output_25_loss: 0.0690 - val_output_26_loss: 0.0143 - val_output_27_loss: 0.1399 - val_output_28_loss: 0.2470 - val_output_29_loss: 0.3968 - val_output_30_loss: 0.0698 - val_output_31_loss: 0.0142 - val_output_32_loss: 0.0306 - val_output_33_loss: 0.1919 - val_output_34_loss: 0.2337\n",
            "Epoch 11: early stopping\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 7min 48s, sys: 1min 17s, total: 9min 5s\n",
            "Wall time: 12min 4s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "from acsa_model import VLSP2018MultiTask\n",
        "from helper import plot_training_history\n",
        "model = VLSP2018MultiTask(PRETRAINED_MODEL, MULTI_BRANCH_NAMES, optimizer, multi_branch=True, name=MODEL_NAME)\n",
        "\n",
        "history = model.fit(\n",
        "    train_tf_dataset,\n",
        "    validation_data = val_tf_dataset,\n",
        "    callbacks = [early_stop_callback],\n",
        "    epochs = EPOCHS,\n",
        "    verbose = 1\n",
        ").history\n",
        "\n",
        "model.save_weights(f'./weights/{MODEL_NAME}/{MODEL_NAME}', save_format='tf')\n",
        "plot_training_history(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bekGxelJmM6g",
        "outputId": "63cb56b5-b56d-4e12-ed64-4380a29a026a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/GoogleDrive\n",
            "  adding: weights/Hotel-v2/ (stored 0%)\n",
            "  adding: weights/Hotel-v2/Hotel-v2.index (deflated 80%)\n",
            "  adding: weights/Hotel-v2/checkpoint (deflated 37%)\n",
            "  adding: weights/Hotel-v2/Hotel-v2.data-00000-of-00001 (deflated 32%)\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive # Move weights to Drive for faster downloading\n",
        "drive.mount('/content/GoogleDrive', force_remount=True)\n",
        "!zip -r ./weights/{MODEL_NAME}.zip ./weights/{MODEL_NAME}\n",
        "!mv ./weights/{MODEL_NAME}.zip /content/GoogleDrive/My\\ Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELoAR9-Y9kSF"
      },
      "source": [
        "# Predict on the Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkqO1rs0Qrw0",
        "outputId": "6439a4cb-8706-4169-9770-d07a9490c989"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at vinai/phobert-base were not used when initializing TFRobertaModel: ['lm_head']\n",
            "- This IS expected if you are initializing TFRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFRobertaModel were initialized from the model checkpoint at vinai/phobert-base.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 35s 120ms/step - loss: 4.9794 - output_1_loss: 0.0324 - output_2_loss: 0.1587 - output_3_loss: 0.3067 - output_4_loss: 0.0956 - output_5_loss: 0.0250 - output_6_loss: 0.0721 - output_7_loss: 0.1041 - output_8_loss: 0.0337 - output_9_loss: 0.0623 - output_10_loss: 0.1199 - output_11_loss: 0.1733 - output_12_loss: 0.0910 - output_13_loss: 0.4497 - output_14_loss: 0.2728 - output_15_loss: 0.2372 - output_16_loss: 0.3162 - output_17_loss: 0.1048 - output_18_loss: 0.0591 - output_19_loss: 0.1324 - output_20_loss: 0.1397 - output_21_loss: 0.2257 - output_22_loss: 0.2394 - output_23_loss: 0.1298 - output_24_loss: 0.0223 - output_25_loss: 0.0663 - output_26_loss: 0.0142 - output_27_loss: 0.1331 - output_28_loss: 0.2452 - output_29_loss: 0.3967 - output_30_loss: 0.0661 - output_31_loss: 0.0138 - output_32_loss: 0.0300 - output_33_loss: 0.1854 - output_34_loss: 0.2245\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[4.979374885559082,\n",
              " 0.03243863210082054,\n",
              " 0.15866489708423615,\n",
              " 0.3067288398742676,\n",
              " 0.09564558416604996,\n",
              " 0.025027627125382423,\n",
              " 0.0721396654844284,\n",
              " 0.10414005815982819,\n",
              " 0.03374325856566429,\n",
              " 0.06234736368060112,\n",
              " 0.11988642066717148,\n",
              " 0.1732860654592514,\n",
              " 0.09102153033018112,\n",
              " 0.44972583651542664,\n",
              " 0.27279847860336304,\n",
              " 0.23720715939998627,\n",
              " 0.31621435284614563,\n",
              " 0.1047910749912262,\n",
              " 0.05914295092225075,\n",
              " 0.13236814737319946,\n",
              " 0.13967794179916382,\n",
              " 0.2256615310907364,\n",
              " 0.23936797678470612,\n",
              " 0.12975770235061646,\n",
              " 0.022257719188928604,\n",
              " 0.06630702316761017,\n",
              " 0.014179110527038574,\n",
              " 0.13307131826877594,\n",
              " 0.24521803855895996,\n",
              " 0.396674245595932,\n",
              " 0.06608866900205612,\n",
              " 0.013829930685460567,\n",
              " 0.02999497763812542,\n",
              " 0.18542535603046417,\n",
              " 0.22454629838466644]"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from acsa_model import VLSP2018MultiTask\n",
        "reloaded_model = VLSP2018MultiTask(PRETRAINED_MODEL, MULTI_BRANCH_NAMES, optimizer, multi_branch=True, name=MODEL_NAME)\n",
        "reloaded_model.load_weights(f'./weights/{MODEL_NAME}/{MODEL_NAME}') # Reload the Model\n",
        "reloaded_model.evaluate(val_tf_dataset, batch_size=BATCH_SIZE, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuW9rBsnWIJo",
        "outputId": "3fa5b33e-ba2c-411a-9931-2abc1791fab4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "24/24 [==============================] - 16s 143ms/step\n",
            "24/24 [==============================] - 8s 132ms/step - loss: 6.0214 - output_1_loss: 0.0405 - output_2_loss: 0.1821 - output_3_loss: 0.3147 - output_4_loss: 0.1277 - output_5_loss: 0.0990 - output_6_loss: 0.0789 - output_7_loss: 0.1962 - output_8_loss: 0.0372 - output_9_loss: 0.0592 - output_10_loss: 0.1478 - output_11_loss: 0.2015 - output_12_loss: 0.1090 - output_13_loss: 0.3056 - output_14_loss: 0.1576 - output_15_loss: 0.2441 - output_16_loss: 0.5600 - output_17_loss: 0.1500 - output_18_loss: 0.0727 - output_19_loss: 0.1651 - output_20_loss: 0.2198 - output_21_loss: 0.2491 - output_22_loss: 0.4156 - output_23_loss: 0.2582 - output_24_loss: 0.0567 - output_25_loss: 0.0974 - output_26_loss: 0.0435 - output_27_loss: 0.1479 - output_28_loss: 0.2254 - output_29_loss: 0.4184 - output_30_loss: 0.0940 - output_31_loss: 0.0403 - output_32_loss: 0.0165 - output_33_loss: 0.2696 - output_34_loss: 0.2202\n",
            "Example: Ga giường không sạch, nhân viên quên dọn phòng một ngày.\n",
            "=> ROOMS#CLEANLINESS,negative\n",
            "=> ROOM_AMENITIES#CLEANLINESS,negative\n",
            "=> SERVICE#GENERAL,negative\n"
          ]
        }
      ],
      "source": [
        "y_pred = reloaded_model.acsa_predict(test_tf_dataset, BATCH_SIZE) # On test set\n",
        "reloaded_model.evaluate(test_tf_dataset, batch_size=BATCH_SIZE, verbose=1)\n",
        "print('Example:', raw_datasets['test'][0]['Review'])\n",
        "reloaded_model.print_acsa_pred(y_pred[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2C-dHffWIJp",
        "outputId": "83a8710b-b9c1-41ed-f03c-4818d524bcb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your review: Lễ tân thân thiện, có thang máy, vị trí ks thuận tiện, view thành phố rất đẹp. Phòng sạch nhưng hơi nhỏ & thiếu bình đun siêu tốc. Sẽ quay lại & giới thiệu bạn bè\n",
            "[INFO] Preprocessing and tokenizing text data...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 7s 7s/step\n",
            "=> HOTEL#GENERAL,positive\n",
            "=> LOCATION#GENERAL,positive\n",
            "=> ROOMS#CLEANLINESS,positive\n",
            "=> ROOMS#DESIGN&FEATURES,negative\n",
            "=> ROOM_AMENITIES#DESIGN&FEATURES,negative\n",
            "=> SERVICE#GENERAL,positive\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.data import Dataset # On a random review by user\n",
        "random_input = VLSP2018Loader.preprocess_and_tokenize(\n",
        "    input('Enter your review: '), vn_preprocessor, tokenizer,\n",
        "    batch_size=1, max_length=MAX_LENGTH\n",
        ")\n",
        "tf_inputs = Dataset.from_tensor_slices({x: [[random_input[x][0]]] for x in tokenizer.model_input_names})\n",
        "random_pred = reloaded_model.acsa_predict(tf_inputs)\n",
        "reloaded_model.print_acsa_pred(random_pred[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AoxIRhDl9BBH"
      },
      "source": [
        "# Scikit-learn Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iFAix4x9PtU7",
        "outputId": "1c0752e2-d547-4338-c9eb-dd33cd860248"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 2],\n",
              "       [0, 0, 0, ..., 0, 0, 1],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 1],\n",
              "       [0, 0, 0, ..., 0, 0, 1],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test = preprocessed_datasets['test'].remove_columns(tokenizer.model_input_names)\n",
        "y_test = y_test.to_pandas().to_numpy()\n",
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u_6eV6CGWIJp",
        "outputId": "9f51bc94-969f-4a9c-9c59-5813e3dd99aa"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 37/37 [00:00<00:00, 51421.22it/s]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "            <div style=\"display: flex; align-items: flex-start; flex-wrap: nowrap\">\n",
              "                <table style=\"margin-right: 10px\">\n",
              "            <tr>\n",
              "                <th style=\"font-weight: bold; text-align: center;\" rowspan=\"2\">ACSA Report (w/o \"None\" polarity)</th>\n",
              "                <th style=\"font-weight: bold; text-align: center;\" colspan=\"4\">Aspect#Category</th>\n",
              "                <th style=\"font-weight: bold; text-align: center;\" colspan=\"4\">Aspect#Category,Polarity</th>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>precision</th><th>recall</th><th>f1-score</th><th>support</th><th>precision</th><th>recall</th><th>f1-score</th><th>support</th>\n",
              "            </tr>\n",
              "        \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#CLEANLINESS,negative</td>\n",
              "                        <td rowspan=\"2\">1.000</td><td rowspan=\"2\">0.400</td><td rowspan=\"2\">0.571</td><td rowspan=\"2\">5</td>\n",
              "                        <td>1.000</td><td>0.667</td><td>0.800</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#CLEANLINESS,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#COMFORT,negative</td>\n",
              "                        <td rowspan=\"2\">0.000</td><td rowspan=\"2\">0.000</td><td rowspan=\"2\">0.000</td><td rowspan=\"2\">26</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>6</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#COMFORT,positive</td>\n",
              "                        \n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>20</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#DESIGN&FEATURES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.780</td><td rowspan=\"3\">0.492</td><td rowspan=\"3\">0.604</td><td rowspan=\"3\">65</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#DESIGN&FEATURES,negative</td>\n",
              "                        \n",
              "                        <td>0.654</td><td>0.773</td><td>0.708</td><td>22</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#DESIGN&FEATURES,positive</td>\n",
              "                        \n",
              "                        <td>0.667</td><td>0.263</td><td>0.377</td><td>38</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.440</td><td rowspan=\"3\">0.524</td><td rowspan=\"3\">0.478</td><td rowspan=\"3\">21</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.435</td><td>0.526</td><td>0.476</td><td>19</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#MISCELLANEOUS,negative</td>\n",
              "                        <td rowspan=\"2\">1.000</td><td rowspan=\"2\">0.000</td><td rowspan=\"2\">0.000</td><td rowspan=\"2\">8</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#MISCELLANEOUS,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#PRICES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.667</td><td rowspan=\"3\">0.154</td><td rowspan=\"3\">0.250</td><td rowspan=\"3\">13</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#PRICES,negative</td>\n",
              "                        \n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#PRICES,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.250</td><td>0.400</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#QUALITY,neutral</td>\n",
              "                        <td rowspan=\"3\">0.917</td><td rowspan=\"3\">0.647</td><td rowspan=\"3\">0.759</td><td rowspan=\"3\">51</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#QUALITY,negative</td>\n",
              "                        \n",
              "                        <td>0.741</td><td>0.800</td><td>0.769</td><td>25</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.375</td><td>0.545</td><td>24</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#MISCELLANEOUS,negative</td>\n",
              "                        <td rowspan=\"2\">1.000</td><td rowspan=\"2\">0.000</td><td rowspan=\"2\">0.000</td><td rowspan=\"2\">3</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#MISCELLANEOUS,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#PRICES,negative</td>\n",
              "                        <td rowspan=\"2\">0.000</td><td rowspan=\"2\">0.000</td><td rowspan=\"2\">0.000</td><td rowspan=\"2\">9</td>\n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#PRICES,positive</td>\n",
              "                        \n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>6</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#QUALITY,neutral</td>\n",
              "                        <td rowspan=\"3\">0.945</td><td rowspan=\"3\">0.938</td><td rowspan=\"3\">0.942</td><td rowspan=\"3\">129</td>\n",
              "                        <td>1.000</td><td>0.429</td><td>0.600</td><td>14</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#QUALITY,negative</td>\n",
              "                        \n",
              "                        <td>0.850</td><td>0.895</td><td>0.872</td><td>19</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>0.912</td><td>0.969</td><td>0.939</td><td>96</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#STYLE&OPTIONS,neutral</td>\n",
              "                        <td rowspan=\"3\">0.920</td><td rowspan=\"3\">0.831</td><td rowspan=\"3\">0.873</td><td rowspan=\"3\">124</td>\n",
              "                        <td>1.000</td><td>0.167</td><td>0.286</td><td>6</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#STYLE&OPTIONS,negative</td>\n",
              "                        \n",
              "                        <td>0.875</td><td>0.840</td><td>0.857</td><td>75</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#STYLE&OPTIONS,positive</td>\n",
              "                        \n",
              "                        <td>0.846</td><td>0.767</td><td>0.805</td><td>43</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#CLEANLINESS,negative</td>\n",
              "                        <td rowspan=\"2\">0.873</td><td rowspan=\"2\">0.821</td><td rowspan=\"2\">0.846</td><td rowspan=\"2\">67</td>\n",
              "                        <td>0.500</td><td>0.250</td><td>0.333</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#CLEANLINESS,positive</td>\n",
              "                        \n",
              "                        <td>0.898</td><td>0.898</td><td>0.898</td><td>59</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#COMFORT,neutral</td>\n",
              "                        <td rowspan=\"3\">0.768</td><td rowspan=\"3\">0.670</td><td rowspan=\"3\">0.716</td><td rowspan=\"3\">94</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#COMFORT,negative</td>\n",
              "                        \n",
              "                        <td>0.125</td><td>0.077</td><td>0.095</td><td>13</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#COMFORT,positive</td>\n",
              "                        \n",
              "                        <td>0.797</td><td>0.738</td><td>0.766</td><td>80</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#DESIGN&FEATURES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.831</td><td rowspan=\"3\">0.812</td><td rowspan=\"3\">0.821</td><td rowspan=\"3\">85</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#DESIGN&FEATURES,negative</td>\n",
              "                        \n",
              "                        <td>0.375</td><td>0.375</td><td>0.375</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#DESIGN&FEATURES,positive</td>\n",
              "                        \n",
              "                        <td>0.853</td><td>0.865</td><td>0.859</td><td>74</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.887</td><td rowspan=\"3\">0.834</td><td rowspan=\"3\">0.860</td><td rowspan=\"3\">151</td>\n",
              "                        <td>0.500</td><td>0.250</td><td>0.333</td><td>4</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.750</td><td>0.500</td><td>0.600</td><td>12</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.879</td><td>0.859</td><td>0.869</td><td>135</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#MISCELLANEOUS,neutral</td>\n",
              "                        <td rowspan=\"3\">0.562</td><td rowspan=\"3\">0.132</td><td rowspan=\"3\">0.214</td><td rowspan=\"3\">68</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#MISCELLANEOUS,negative</td>\n",
              "                        \n",
              "                        <td>0.438</td><td>0.206</td><td>0.280</td><td>34</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#MISCELLANEOUS,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>29</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#PRICES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.808</td><td rowspan=\"3\">0.831</td><td rowspan=\"3\">0.819</td><td rowspan=\"3\">71</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#PRICES,negative</td>\n",
              "                        \n",
              "                        <td>0.300</td><td>0.500</td><td>0.375</td><td>6</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#PRICES,positive</td>\n",
              "                        \n",
              "                        <td>0.841</td><td>0.841</td><td>0.841</td><td>63</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#QUALITY,neutral</td>\n",
              "                        <td rowspan=\"3\">0.667</td><td rowspan=\"3\">0.462</td><td rowspan=\"3\">0.545</td><td rowspan=\"3\">13</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#QUALITY,negative</td>\n",
              "                        \n",
              "                        <td>0.400</td><td>0.400</td><td>0.400</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>0.750</td><td>0.429</td><td>0.545</td><td>7</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.968</td><td rowspan=\"3\">0.964</td><td rowspan=\"3\">0.966</td><td rowspan=\"3\">221</td>\n",
              "                        <td>0.667</td><td>0.133</td><td>0.222</td><td>15</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.545</td><td>0.750</td><td>0.632</td><td>16</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.949</td><td>0.974</td><td>0.961</td><td>190</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#CLEANLINESS,neutral</td>\n",
              "                        <td rowspan=\"3\">0.922</td><td rowspan=\"3\">0.940</td><td rowspan=\"3\">0.931</td><td rowspan=\"3\">200</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>7</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#CLEANLINESS,negative</td>\n",
              "                        \n",
              "                        <td>0.840</td><td>0.778</td><td>0.808</td><td>27</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#CLEANLINESS,positive</td>\n",
              "                        \n",
              "                        <td>0.899</td><td>0.970</td><td>0.933</td><td>166</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#COMFORT,neutral</td>\n",
              "                        <td rowspan=\"3\">0.737</td><td rowspan=\"3\">0.785</td><td rowspan=\"3\">0.760</td><td rowspan=\"3\">93</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#COMFORT,negative</td>\n",
              "                        \n",
              "                        <td>0.630</td><td>0.548</td><td>0.586</td><td>31</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#COMFORT,positive</td>\n",
              "                        \n",
              "                        <td>0.736</td><td>0.883</td><td>0.803</td><td>60</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#DESIGN&FEATURES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.909</td><td rowspan=\"3\">0.854</td><td rowspan=\"3\">0.880</td><td rowspan=\"3\">198</td>\n",
              "                        <td>0.533</td><td>0.381</td><td>0.444</td><td>21</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#DESIGN&FEATURES,negative</td>\n",
              "                        \n",
              "                        <td>0.889</td><td>0.706</td><td>0.787</td><td>68</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#DESIGN&FEATURES,positive</td>\n",
              "                        \n",
              "                        <td>0.786</td><td>0.844</td><td>0.814</td><td>109</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.722</td><td rowspan=\"3\">0.456</td><td rowspan=\"3\">0.559</td><td rowspan=\"3\">57</td>\n",
              "                        <td>0.500</td><td>0.200</td><td>0.286</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.100</td><td>0.182</td><td>10</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.667</td><td>0.524</td><td>0.587</td><td>42</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#MISCELLANEOUS,neutral</td>\n",
              "                        <td rowspan=\"3\">1.000</td><td rowspan=\"3\">0.000</td><td rowspan=\"3\">0.000</td><td rowspan=\"3\">4</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#MISCELLANEOUS,negative</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#MISCELLANEOUS,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#PRICES,negative</td>\n",
              "                        <td rowspan=\"2\">0.778</td><td rowspan=\"2\">0.724</td><td rowspan=\"2\">0.750</td><td rowspan=\"2\">29</td>\n",
              "                        <td>0.500</td><td>0.500</td><td>0.500</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#PRICES,positive</td>\n",
              "                        \n",
              "                        <td>0.789</td><td>0.714</td><td>0.750</td><td>21</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#QUALITY,negative</td>\n",
              "                        <td rowspan=\"2\">1.000</td><td rowspan=\"2\">0.400</td><td rowspan=\"2\">0.571</td><td rowspan=\"2\">10</td>\n",
              "                        <td>1.000</td><td>0.500</td><td>0.667</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.375</td><td>0.545</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#CLEANLINESS,negative</td>\n",
              "                        <td rowspan=\"2\">0.750</td><td rowspan=\"2\">0.457</td><td rowspan=\"2\">0.568</td><td rowspan=\"2\">46</td>\n",
              "                        <td>0.696</td><td>0.552</td><td>0.615</td><td>29</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#CLEANLINESS,positive</td>\n",
              "                        \n",
              "                        <td>0.400</td><td>0.118</td><td>0.182</td><td>17</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#COMFORT,neutral</td>\n",
              "                        <td rowspan=\"3\">0.853</td><td rowspan=\"3\">0.426</td><td rowspan=\"3\">0.569</td><td rowspan=\"3\">68</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#COMFORT,negative</td>\n",
              "                        \n",
              "                        <td>0.600</td><td>0.143</td><td>0.231</td><td>21</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#COMFORT,positive</td>\n",
              "                        \n",
              "                        <td>0.793</td><td>0.523</td><td>0.630</td><td>44</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#DESIGN&FEATURES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.817</td><td rowspan=\"3\">0.618</td><td rowspan=\"3\">0.704</td><td rowspan=\"3\">144</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#DESIGN&FEATURES,negative</td>\n",
              "                        \n",
              "                        <td>0.772</td><td>0.575</td><td>0.659</td><td>106</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#DESIGN&FEATURES,positive</td>\n",
              "                        \n",
              "                        <td>0.633</td><td>0.576</td><td>0.603</td><td>33</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#GENERAL,negative</td>\n",
              "                        <td rowspan=\"2\">0.719</td><td rowspan=\"2\">0.719</td><td rowspan=\"2\">0.719</td><td rowspan=\"2\">32</td>\n",
              "                        <td>0.200</td><td>1.000</td><td>0.333</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.815</td><td>0.710</td><td>0.759</td><td>31</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#MISCELLANEOUS,negative</td>\n",
              "                        <td rowspan=\"1\">1.000</td><td rowspan=\"1\">0.000</td><td rowspan=\"1\">0.000</td><td rowspan=\"1\">3</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#PRICES,negative</td>\n",
              "                        <td rowspan=\"1\">1.000</td><td rowspan=\"1\">0.000</td><td rowspan=\"1\">0.000</td><td rowspan=\"1\">1</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#QUALITY,negative</td>\n",
              "                        <td rowspan=\"2\">0.630</td><td rowspan=\"2\">0.492</td><td rowspan=\"2\">0.552</td><td rowspan=\"2\">59</td>\n",
              "                        <td>0.579</td><td>0.688</td><td>0.629</td><td>32</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>0.750</td><td>0.222</td><td>0.343</td><td>27</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.976</td><td rowspan=\"3\">0.983</td><td rowspan=\"3\">0.980</td><td rowspan=\"3\">416</td>\n",
              "                        <td>0.643</td><td>0.321</td><td>0.429</td><td>28</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.705</td><td>0.795</td><td>0.747</td><td>39</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.950</td><td>0.983</td><td>0.966</td><td>349</td>\n",
              "                    </tr>\n",
              "                </table> \n",
              "                <div style=\"display: flex; align-items: center; flex-direction: column\">\n",
              "                    <b>Polarity Report</b><br>\n",
              "                    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>None</th>\n",
              "      <td>0.966974</td>\n",
              "      <td>0.984396</td>\n",
              "      <td>0.975607</td>\n",
              "      <td>17816.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>0.865236</td>\n",
              "      <td>0.803433</td>\n",
              "      <td>0.833190</td>\n",
              "      <td>1806.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>0.701657</td>\n",
              "      <td>0.590698</td>\n",
              "      <td>0.641414</td>\n",
              "      <td>645.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>0.651163</td>\n",
              "      <td>0.210526</td>\n",
              "      <td>0.318182</td>\n",
              "      <td>133.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.950882</td>\n",
              "      <td>0.950882</td>\n",
              "      <td>0.950882</td>\n",
              "      <td>0.950882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.796257</td>\n",
              "      <td>0.647263</td>\n",
              "      <td>0.692098</td>\n",
              "      <td>20400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.947519</td>\n",
              "      <td>0.950882</td>\n",
              "      <td>0.948146</td>\n",
              "      <td>20400.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br>\n",
              "                    <b>Macro Avg Report</b><br>\n",
              "                    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Aspect#Category,Polarity</th>\n",
              "      <td>0.951</td>\n",
              "      <td>0.816</td>\n",
              "      <td>0.540</td>\n",
              "      <td>0.552</td>\n",
              "      <td>20400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aspect#Category</th>\n",
              "      <td>0.957</td>\n",
              "      <td>0.795</td>\n",
              "      <td>0.524</td>\n",
              "      <td>0.565</td>\n",
              "      <td>20400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Polarity</th>\n",
              "      <td>0.951</td>\n",
              "      <td>0.796</td>\n",
              "      <td>0.647</td>\n",
              "      <td>0.692</td>\n",
              "      <td>20400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "                </div>\n",
              "            </div>\n",
              "        "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from evaluators.sklearn_evaluator import VLSP2018SklearnEvaluator\n",
        "sk_eval = VLSP2018SklearnEvaluator(y_test, y_pred, list(MULTI_BRANCH_NAMES))\n",
        "# sk_eval.report(report_type='Aspect#Category,Polarity').round(3)\n",
        "# sk_eval.report(report_type='Aspect#Category').round(3)\n",
        "# sk_eval.report(report_type='Polarity').round(3)\n",
        "# sk_eval.report(report_type='macro_avg').round(3)\n",
        "sk_eval.report(report_type='all')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLhItgRyWIJp"
      },
      "source": [
        "# VLSP Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yt4As64AoFYz",
        "outputId": "605644de-1dfb-42c6-dc73-7b1faa92819f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "600it [00:00, 64847.00it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row mismatch:﻿#1 <-> #1\n",
            "Evaluation Result >> File:./predictions/Hotel-v2.txt<> [../datasets/vlsp2018_hotel/3-VLSP2018-SA-Hotel-test.txt]\n",
            "                    Gold count\t46\t416\t200\t93\t221\t57\t198\t67\t68\t144\t32\t124\t10\t65\t85\t51\t13\t71\t151\t29\t94\t21\t68\t59\t8\t26\t129\t3\t13\t9\t5\t4\t3\t1\n",
            "                  ANSWER count\t28\t419\t204\t99\t220\t36\t186\t63\t34\t109\t32\t112\t4\t41\t83\t36\t9\t73\t142\t27\t82\t25\t16\t46\t0\t1\t128\t0\t3\t3\t2\t0\t0\t0\n",
            "\n",
            "        Correct ANSWER: aspect\t20\t408\t188\t73\t213\t26\t169\t55\t29\t89\t23\t103\t4\t32\t69\t33\t6\t59\t126\t21\t63\t11\t9\t29\t0\t0\t121\t0\t2\t0\t2\t0\t0\t0\n",
            "             Precision: aspect\t0.7143\t0.9737\t0.9216\t0.7374\t0.9682\t0.7222\t0.9086\t0.8730\t0.8529\t0.8165\t0.7188\t0.9196\t1.0000\t0.7805\t0.8313\t0.9167\t0.6667\t0.8082\t0.8873\t0.7778\t0.7683\t0.4400\t0.5625\t0.6304\t0.0000\t0.0000\t0.9453\t0.0000\t0.6667\t0.0000\t1.0000\t0.0000\t0.0000\t0.0000\n",
            "                Recall: aspect\t0.4348\t0.9808\t0.9400\t0.7849\t0.9638\t0.4561\t0.8535\t0.8209\t0.4265\t0.6181\t0.7188\t0.8306\t0.4000\t0.4923\t0.8118\t0.6471\t0.4615\t0.8310\t0.8344\t0.7241\t0.6702\t0.5238\t0.1324\t0.4915\t0.0000\t0.0000\t0.9380\t0.0000\t0.1538\t0.0000\t0.4000\t0.0000\t0.0000\t0.0000\n",
            "              F1 score: aspect\t0.5405\t0.9772\t0.9307\t0.7604\t0.9660\t0.5591\t0.8802\t0.8462\t0.5686\t0.7036\t0.7188\t0.8729\t0.5714\t0.6038\t0.8214\t0.7586\t0.5455\t0.8194\t0.8601\t0.7500\t0.7159\t0.4783\t0.2143\t0.5524\t0.0000\t0.0000\t0.9416\t0.0000\t0.2500\t0.0000\t0.5714\t0.0000\t0.0000\t0.0000\n",
            "\n",
            "  Over All ANSWER: aspect:----\t0.8763\t0.7674\t0.8182\n",
            "\n",
            "  Correct ANSWER: aspect,value\t17\t382\t182\t70\t199\t24\t148\t55\t26\t80\t23\t97\t4\t27\t67\t29\t5\t56\t123\t19\t60\t10\t7\t28\t0\t0\t116\t0\t2\t0\t2\t0\t0\t0\n",
            "      Precision: aspect, value\t0.6071\t0.9117\t0.8922\t0.7071\t0.9045\t0.6667\t0.7957\t0.8730\t0.7647\t0.7339\t0.7188\t0.8661\t1.0000\t0.6585\t0.8072\t0.8056\t0.5556\t0.7671\t0.8662\t0.7037\t0.7317\t0.4000\t0.4375\t0.6087\t0.0000\t0.0000\t0.9063\t0.0000\t0.6667\t0.0000\t1.0000\t0.0000\t0.0000\t0.0000\n",
            "         Recall: aspect, value\t0.3696\t0.9183\t0.9100\t0.7527\t0.9005\t0.4211\t0.7475\t0.8209\t0.3824\t0.5556\t0.7188\t0.7823\t0.4000\t0.4154\t0.7882\t0.5686\t0.3846\t0.7887\t0.8146\t0.6552\t0.6383\t0.4762\t0.1029\t0.4746\t0.0000\t0.0000\t0.8992\t0.0000\t0.1538\t0.0000\t0.4000\t0.0000\t0.0000\t0.0000\n",
            "       F1 score: aspect, value\t0.4595\t0.9150\t0.9010\t0.7292\t0.9025\t0.5161\t0.7708\t0.8462\t0.5098\t0.6324\t0.7188\t0.8220\t0.5714\t0.5094\t0.7976\t0.6667\t0.4545\t0.7778\t0.8396\t0.6786\t0.6818\t0.4348\t0.1667\t0.5333\t0.0000\t0.0000\t0.9027\t0.0000\t0.2500\t0.0000\t0.5714\t0.0000\t0.0000\t0.0000\n",
            "\n",
            "Over All ANSWER: aspect, value:----\t0.8210\t0.7190\t0.7667\n",
            "\n",
            "\n",
            "asp#1: ROOM_AMENITIES#CLEANLINESS\n",
            "asp#2: SERVICE#GENERAL\n",
            "asp#3: ROOMS#CLEANLINESS\n",
            "asp#4: ROOMS#COMFORT\n",
            "asp#5: LOCATION#GENERAL\n",
            "asp#6: ROOMS#GENERAL\n",
            "asp#7: ROOMS#DESIGN&FEATURES\n",
            "asp#8: HOTEL#CLEANLINESS\n",
            "asp#9: ROOM_AMENITIES#COMFORT\n",
            "asp#10: ROOM_AMENITIES#DESIGN&FEATURES\n",
            "asp#11: ROOM_AMENITIES#GENERAL\n",
            "asp#12: FOOD&DRINKS#STYLE&OPTIONS\n",
            "asp#13: ROOMS#QUALITY\n",
            "asp#14: FACILITIES#DESIGN&FEATURES\n",
            "asp#15: HOTEL#DESIGN&FEATURES\n",
            "asp#16: FACILITIES#QUALITY\n",
            "asp#17: HOTEL#QUALITY\n",
            "asp#18: HOTEL#PRICES\n",
            "asp#19: HOTEL#GENERAL\n",
            "asp#20: ROOMS#PRICES\n",
            "asp#21: HOTEL#COMFORT\n",
            "asp#22: FACILITIES#GENERAL\n",
            "asp#23: HOTEL#MISCELLANEOUS\n",
            "asp#24: ROOM_AMENITIES#QUALITY\n",
            "asp#25: FACILITIES#MISCELLANEOUS\n",
            "asp#26: FACILITIES#COMFORT\n",
            "asp#27: FOOD&DRINKS#QUALITY\n",
            "asp#28: FOOD&DRINKS#MISCELLANEOUS\n",
            "asp#29: FACILITIES#PRICES\n",
            "asp#30: FOOD&DRINKS#PRICES\n",
            "asp#31: FACILITIES#CLEANLINESS\n",
            "asp#32: ROOMS#MISCELLANEOUS\n",
            "asp#33: ROOM_AMENITIES#MISCELLANEOUS\n",
            "asp#34: ROOM_AMENITIES#PRICES\n"
          ]
        }
      ],
      "source": [
        "from processors.vlsp2018_processor import VLSP2018Parser\n",
        "VLSP2018Parser.vlsp_save_as(f'./predictions/{MODEL_NAME}.txt', raw_datasets['test']['Review'], y_pred, MULTI_BRANCH_NAMES)\n",
        "!java ../evaluators/SAEvaluate.java {TEST_PATH.replace('.csv', '.txt')} ./predictions/{MODEL_NAME}.txt"
      ]
    }
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