experiments/Hotel-v1.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": "9c523647-356e-40cd-ef36-c09e8d1a1a2b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m431.4/431.4 kB\u001b[0m \u001b[31m9.6 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[31m42.1 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[31m7.9 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[31m41.3 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[31m18.0 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[31m10.3 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[31m26.3 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[31m19.8 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[31m8.7 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[31m50.0 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": "2b3c099a-a5c7-4abc-e3b6-e0693e5a802d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'absa-vlsp-2018'...\n",
            "remote: Enumerating objects: 204, done.\u001b[K\n",
            "remote: Counting objects: 100% (115/115), done.\u001b[K\n",
            "remote: Compressing objects: 100% (86/86), done.\u001b[K\n",
            "remote: Total 204 (delta 53), reused 83 (delta 26), pack-reused 89\u001b[K\n",
            "Receiving objects: 100% (204/204), 30.69 MiB | 16.27 MiB/s, done.\n",
            "Resolving deltas: 100% (70/70), 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": "a612cb5d-1283-4355-be16-31f9593ede33"
      },
      "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": "b23b612e-c5f5-45f0-de0f-6c95465339bc"
      },
      "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-v1' # 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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            "6bc18bd9dbc3444e87d5a15c420bd4f7",
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            "615fcbd8c7f949b591573233087a10f1",
            "25f4b5f9c11e4189bf7c7180ee7f0345"
          ]
        },
        "id": "MtJDqdUUWIJk",
        "outputId": "61d35647-7750-4ec0-dc9d-cab38e42192b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34d6d7924baa4d959923dce49051dd60",
              "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": "69198409637546e8b9218269456ab4f3",
              "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": "1ca28ce429044ec2a4ffa158834c9b10",
              "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": "c072f47d-a53f-4fc3-e200-41b861e88d83"
      },
      "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": "f1fda178d7a546a191c79faa3e6eb512",
              "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": "3b5e7d5613b44eafb63077314fd11884",
              "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": {
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              "model_id": "ee870baa10ce410a96976c70f9bd536f",
              "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"
        },
        {
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              "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": {
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              "model_id": "4761596b5c624afd8d0a64916d1d2d2f",
              "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": "4497fb8d252643fb973320d1c7d1e0eb",
              "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": "9ae12c47-b244-4c90-8270-7b4753197a2f"
      },
      "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": "ab246741245e4874afd1667e57a6b7f7",
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            },
            "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": "9bbe1fc9d7c64aa9b301b5d0049e9466",
              "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": "5461c59a6f9b480d870acfb6848454bf",
              "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": "ae78d50826134636bebe60d0ae5345ae",
              "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": 9,
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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": {},
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        },
        {
          "data": {
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              "model_id": "963b8d2eff7348719509eb290e2a7932",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "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": {
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              "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
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            "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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-4f141cb7-877f-411d-98bf-ec204c79a250');\n",
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              "\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "    80% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "  </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": "markdown",
      "metadata": {
        "id": "y-Dk5nixWIJm"
      },
      "source": [
        "# Extra Step for ACSA-v1 Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428,
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        },
        "id": "bSM6s-J4WIJn",
        "outputId": "797d83d2-366c-4d2e-c2fa-7c0711bddd90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] Transforming \"Aspect#Categoy,Polarity\" labels to flattened one-hot encoding...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/multiprocess/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"
          ]
        },
        {
          "data": {
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              "model_id": "ee33ed74f4e04091901482676636461f",
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            "text/plain": [
              "Map (num_proc=8):   0%|          | 0/3000 [00:00<?, ? examples/s]"
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          "metadata": {},
          "output_type": "display_data"
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        {
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            "text/plain": [
              "Map (num_proc=8):   0%|          | 0/2000 [00:00<?, ? examples/s]"
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        {
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            "text/plain": [
              "Map (num_proc=8):   0%|          | 0/600 [00:00<?, ? examples/s]"
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        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['FlattenOneHotLabels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 3000\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['FlattenOneHotLabels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['FlattenOneHotLabels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 600\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_from_disk\n",
        "preprocessed_datasets = load_from_disk('../datasets/preprocessed_hotel')\n",
        "preprocessed_datasets = VLSP2018Loader.labels_to_flatten_onehot(preprocessed_datasets)\n",
        "preprocessed_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pqwqSpMWIJn",
        "outputId": "e17b93ec-5fe5-4a12-b9f6-a48da5a41d1a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Aspect#Category,Polarity in One-hot form of the first review:\n"
          ]
        },
        {
          "data": {
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              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0]])"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Aspect#Category,Polarity in One-hot form of the first review:')\n",
        "example_onehot = preprocessed_datasets['train'][0]['FlattenOneHotLabels']\n",
        "np.array(example_onehot).reshape(-1, 4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGRUGqTWWIJn"
      },
      "source": [
        "# Prepare for TensorFlow Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCWAdAozDcAE",
        "outputId": "e99c28cc-eb89-4403-e228-25dea5f5faa9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['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"
          ]
        }
      ],
      "source": [
        "ASPECT_CATEGORY_NAMES = raw_datasets['train'].column_names[1:]\n",
        "steps_per_epoch = len(preprocessed_datasets['train']) // BATCH_SIZE\n",
        "total_steps = EPOCHS * steps_per_epoch\n",
        "print(ASPECT_CATEGORY_NAMES)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "AWlh8K7OWIJn"
      },
      "outputs": [],
      "source": [
        "train_tf_dataset = preprocessed_datasets['train'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols='FlattenOneHotLabels',\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='FlattenOneHotLabels',\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='FlattenOneHotLabels',\n",
        "    batch_size=BATCH_SIZE, shuffle=False, num_workers=8\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "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": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ots6QvfKNS9E",
        "outputId": "49caeaea-3c41-43af-fa4a-66550ddb4047"
      },
      "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_6/roberta/pooler/dense/kernel:0', 'tf_roberta_model_6/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_6/roberta/pooler/dense/kernel:0', 'tf_roberta_model_6/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_6/roberta/pooler/dense/kernel:0', 'tf_roberta_model_6/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_6/roberta/pooler/dense/kernel:0', 'tf_roberta_model_6/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 [==============================] - 134s 557ms/step - loss: 0.1860 - val_loss: 0.1323\n",
            "Epoch 2/20\n",
            "120/120 [==============================] - 58s 447ms/step - loss: 0.1293 - val_loss: 0.0979\n",
            "Epoch 3/20\n",
            "120/120 [==============================] - 58s 441ms/step - loss: 0.0939 - val_loss: 0.0836\n",
            "Epoch 4/20\n",
            "120/120 [==============================] - 57s 438ms/step - loss: 0.0708 - val_loss: 0.0748\n",
            "Epoch 5/20\n",
            "120/120 [==============================] - 57s 438ms/step - loss: 0.0496 - val_loss: 0.0770\n",
            "Epoch 6/20\n",
            "120/120 [==============================] - 58s 442ms/step - loss: 0.0312 - val_loss: 0.0727\n",
            "Epoch 7/20\n",
            "120/120 [==============================] - 58s 437ms/step - loss: 0.0172 - val_loss: 0.0720\n",
            "Epoch 8/20\n",
            "120/120 [==============================] - 57s 437ms/step - loss: 0.0097 - val_loss: 0.0710\n",
            "Epoch 9/20\n",
            "120/120 [==============================] - 57s 434ms/step - loss: 0.0068 - val_loss: 0.0718\n",
            "Epoch 10/20\n",
            "120/120 [==============================] - 57s 438ms/step - loss: 0.0059 - val_loss: 0.0733\n",
            "Epoch 11/20\n",
            "120/120 [==============================] - ETA: 0s - loss: 0.0054Restoring model weights from the end of the best epoch: 8.\n",
            "120/120 [==============================] - 58s 442ms/step - loss: 0.0054 - val_loss: 0.0725\n",
            "Epoch 11: early stopping\n"
          ]
        },
        {
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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 2s, sys: 1min 4s, total: 8min 6s\n",
            "Wall time: 11min 53s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "from acsa_model import VLSP2018MultiTask\n",
        "from helper import plot_training_history\n",
        "model = VLSP2018MultiTask(PRETRAINED_MODEL, ASPECT_CATEGORY_NAMES, optimizer, 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": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bekGxelJmM6g",
        "outputId": "5fdf703b-f117-4904-e972-20e90c2a9ee7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/GoogleDrive\n",
            "  adding: weights/Hotel-v1/ (stored 0%)\n",
            "  adding: weights/Hotel-v1/checkpoint (deflated 37%)\n",
            "  adding: weights/Hotel-v1/Hotel-v1.index (deflated 80%)\n",
            "  adding: weights/Hotel-v1/Hotel-v1.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": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkqO1rs0Qrw0",
        "outputId": "7243fbc9-a754-4fc7-94c3-f56b407fc3b9"
      },
      "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 [==============================] - 32s 120ms/step - loss: 0.0710\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.07096453756093979"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from acsa_model import VLSP2018MultiTask\n",
        "reloaded_model = VLSP2018MultiTask(PRETRAINED_MODEL, ASPECT_CATEGORY_NAMES, optimizer, 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": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuW9rBsnWIJo",
        "outputId": "144bb885-9cea-4815-aaa0-f34d38288b0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "24/24 [==============================] - 17s 163ms/step\n",
            "24/24 [==============================] - 8s 136ms/step - loss: 0.0853\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": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2C-dHffWIJp",
        "outputId": "1ffa15b5-a60e-4d3b-9ba4-61186473af34"
      },
      "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",
            "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": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFTFgzTNWIJp",
        "outputId": "4499cc08-cdbf-4459-db83-95acb0ac178e"
      },
      "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": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from helper import argmax_label_matrix\n",
        "y_test = argmax_label_matrix(preprocessed_datasets['test']['FlattenOneHotLabels'])\n",
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u_6eV6CGWIJp",
        "outputId": "3b963589-70cb-4fe2-b68a-8a272f62b974"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 37/37 [00:00<00:00, 50815.08it/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,positive</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>0.000</td><td>0.000</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#CLEANLINESS,negative</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.667</td><td>0.800</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#COMFORT,positive</td>\n",
              "                        <td rowspan=\"2\">1.000</td><td rowspan=\"2\">0.192</td><td rowspan=\"2\">0.323</td><td rowspan=\"2\">26</td>\n",
              "                        <td>1.000</td><td>0.200</td><td>0.333</td><td>20</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#COMFORT,negative</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>FACILITIES#DESIGN&FEATURES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.769</td><td rowspan=\"3\">0.462</td><td rowspan=\"3\">0.577</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,positive</td>\n",
              "                        \n",
              "                        <td>0.611</td><td>0.289</td><td>0.393</td><td>38</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#DESIGN&FEATURES,negative</td>\n",
              "                        \n",
              "                        <td>0.524</td><td>0.500</td><td>0.512</td><td>22</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.706</td><td rowspan=\"3\">0.571</td><td rowspan=\"3\">0.632</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,positive</td>\n",
              "                        \n",
              "                        <td>0.667</td><td>0.526</td><td>0.588</td><td>19</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.500</td><td>1.000</td><td>0.667</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#MISCELLANEOUS,positive</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>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#MISCELLANEOUS,negative</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#PRICES,neutral</td>\n",
              "                        <td rowspan=\"3\">1.000</td><td rowspan=\"3\">0.077</td><td rowspan=\"3\">0.143</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,positive</td>\n",
              "                        \n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#PRICES,negative</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#QUALITY,neutral</td>\n",
              "                        <td rowspan=\"3\">0.931</td><td rowspan=\"3\">0.529</td><td rowspan=\"3\">0.675</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,positive</td>\n",
              "                        \n",
              "                        <td>0.889</td><td>0.333</td><td>0.485</td><td>24</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FACILITIES#QUALITY,negative</td>\n",
              "                        \n",
              "                        <td>0.850</td><td>0.680</td><td>0.756</td><td>25</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#MISCELLANEOUS,positive</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>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#MISCELLANEOUS,negative</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>FOOD&DRINKS#PRICES,positive</td>\n",
              "                        <td rowspan=\"2\">0.250</td><td rowspan=\"2\">0.111</td><td rowspan=\"2\">0.154</td><td rowspan=\"2\">9</td>\n",
              "                        <td>0.500</td><td>0.167</td><td>0.250</td><td>6</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#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>FOOD&DRINKS#QUALITY,neutral</td>\n",
              "                        <td rowspan=\"3\">0.927</td><td rowspan=\"3\">0.891</td><td rowspan=\"3\">0.909</td><td rowspan=\"3\">129</td>\n",
              "                        <td>0.750</td><td>0.214</td><td>0.333</td><td>14</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>0.892</td><td>0.948</td><td>0.919</td><td>96</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#QUALITY,negative</td>\n",
              "                        \n",
              "                        <td>0.778</td><td>0.737</td><td>0.757</td><td>19</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#STYLE&OPTIONS,neutral</td>\n",
              "                        <td rowspan=\"3\">0.920</td><td rowspan=\"3\">0.839</td><td rowspan=\"3\">0.878</td><td rowspan=\"3\">124</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>6</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#STYLE&OPTIONS,positive</td>\n",
              "                        \n",
              "                        <td>0.900</td><td>0.837</td><td>0.867</td><td>43</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD&DRINKS#STYLE&OPTIONS,negative</td>\n",
              "                        \n",
              "                        <td>0.863</td><td>0.840</td><td>0.851</td><td>75</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#CLEANLINESS,positive</td>\n",
              "                        <td rowspan=\"2\">0.859</td><td rowspan=\"2\">0.821</td><td rowspan=\"2\">0.840</td><td rowspan=\"2\">67</td>\n",
              "                        <td>0.857</td><td>0.915</td><td>0.885</td><td>59</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#CLEANLINESS,negative</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.125</td><td>0.222</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#COMFORT,neutral</td>\n",
              "                        <td rowspan=\"3\">0.769</td><td rowspan=\"3\">0.638</td><td rowspan=\"3\">0.698</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,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#COMFORT,negative</td>\n",
              "                        \n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>13</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#DESIGN&FEATURES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.840</td><td rowspan=\"3\">0.741</td><td rowspan=\"3\">0.788</td><td rowspan=\"3\">85</td>\n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#DESIGN&FEATURES,positive</td>\n",
              "                        \n",
              "                        <td>0.817</td><td>0.784</td><td>0.800</td><td>74</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#DESIGN&FEATURES,negative</td>\n",
              "                        \n",
              "                        <td>0.500</td><td>0.125</td><td>0.200</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.896</td><td rowspan=\"3\">0.795</td><td rowspan=\"3\">0.842</td><td rowspan=\"3\">151</td>\n",
              "                        <td>1.000</td><td>0.250</td><td>0.400</td><td>4</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.881</td><td>0.822</td><td>0.851</td><td>135</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.857</td><td>0.500</td><td>0.632</td><td>12</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#MISCELLANEOUS,neutral</td>\n",
              "                        <td rowspan=\"3\">0.778</td><td rowspan=\"3\">0.103</td><td rowspan=\"3\">0.182</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,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#MISCELLANEOUS,negative</td>\n",
              "                        \n",
              "                        <td>0.778</td><td>0.206</td><td>0.326</td><td>34</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#PRICES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.889</td><td rowspan=\"3\">0.789</td><td rowspan=\"3\">0.836</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,positive</td>\n",
              "                        \n",
              "                        <td>0.862</td><td>0.794</td><td>0.826</td><td>63</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#PRICES,negative</td>\n",
              "                        \n",
              "                        <td>0.400</td><td>0.333</td><td>0.364</td><td>6</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#QUALITY,neutral</td>\n",
              "                        <td rowspan=\"3\">0.667</td><td rowspan=\"3\">0.308</td><td rowspan=\"3\">0.421</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,positive</td>\n",
              "                        \n",
              "                        <td>0.500</td><td>0.286</td><td>0.364</td><td>7</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>HOTEL#QUALITY,negative</td>\n",
              "                        \n",
              "                        <td>0.500</td><td>0.200</td><td>0.286</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.972</td><td rowspan=\"3\">0.955</td><td rowspan=\"3\">0.963</td><td rowspan=\"3\">221</td>\n",
              "                        <td>1.000</td><td>0.067</td><td>0.125</td><td>15</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.940</td><td>0.984</td><td>0.961</td><td>190</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.647</td><td>0.688</td><td>0.667</td><td>16</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#CLEANLINESS,neutral</td>\n",
              "                        <td rowspan=\"3\">0.917</td><td rowspan=\"3\">0.935</td><td rowspan=\"3\">0.926</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,positive</td>\n",
              "                        \n",
              "                        <td>0.895</td><td>0.976</td><td>0.934</td><td>166</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#CLEANLINESS,negative</td>\n",
              "                        \n",
              "                        <td>0.826</td><td>0.704</td><td>0.760</td><td>27</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#COMFORT,neutral</td>\n",
              "                        <td rowspan=\"3\">0.721</td><td rowspan=\"3\">0.860</td><td rowspan=\"3\">0.784</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,positive</td>\n",
              "                        \n",
              "                        <td>0.709</td><td>0.933</td><td>0.806</td><td>60</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#COMFORT,negative</td>\n",
              "                        \n",
              "                        <td>0.625</td><td>0.645</td><td>0.635</td><td>31</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#DESIGN&FEATURES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.917</td><td rowspan=\"3\">0.838</td><td rowspan=\"3\">0.876</td><td rowspan=\"3\">198</td>\n",
              "                        <td>0.500</td><td>0.238</td><td>0.323</td><td>21</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#DESIGN&FEATURES,positive</td>\n",
              "                        \n",
              "                        <td>0.809</td><td>0.853</td><td>0.830</td><td>109</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#DESIGN&FEATURES,negative</td>\n",
              "                        \n",
              "                        <td>0.821</td><td>0.676</td><td>0.742</td><td>68</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.765</td><td rowspan=\"3\">0.456</td><td rowspan=\"3\">0.571</td><td rowspan=\"3\">57</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>5</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#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#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,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#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#PRICES,positive</td>\n",
              "                        <td rowspan=\"2\">0.667</td><td rowspan=\"2\">0.690</td><td rowspan=\"2\">0.678</td><td rowspan=\"2\">29</td>\n",
              "                        <td>0.625</td><td>0.714</td><td>0.667</td><td>21</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#PRICES,negative</td>\n",
              "                        \n",
              "                        <td>0.500</td><td>0.375</td><td>0.429</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#QUALITY,positive</td>\n",
              "                        <td rowspan=\"2\">0.875</td><td rowspan=\"2\">0.700</td><td rowspan=\"2\">0.778</td><td rowspan=\"2\">10</td>\n",
              "                        <td>0.857</td><td>0.750</td><td>0.800</td><td>8</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOMS#QUALITY,negative</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.500</td><td>0.667</td><td>2</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#CLEANLINESS,positive</td>\n",
              "                        <td rowspan=\"2\">0.630</td><td rowspan=\"2\">0.370</td><td rowspan=\"2\">0.466</td><td rowspan=\"2\">46</td>\n",
              "                        <td>0.250</td><td>0.059</td><td>0.095</td><td>17</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#CLEANLINESS,negative</td>\n",
              "                        \n",
              "                        <td>0.609</td><td>0.483</td><td>0.538</td><td>29</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#COMFORT,neutral</td>\n",
              "                        <td rowspan=\"3\">0.861</td><td rowspan=\"3\">0.456</td><td rowspan=\"3\">0.596</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,positive</td>\n",
              "                        \n",
              "                        <td>0.833</td><td>0.568</td><td>0.676</td><td>44</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#COMFORT,negative</td>\n",
              "                        \n",
              "                        <td>0.667</td><td>0.190</td><td>0.296</td><td>21</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#DESIGN&FEATURES,neutral</td>\n",
              "                        <td rowspan=\"3\">0.880</td><td rowspan=\"3\">0.611</td><td rowspan=\"3\">0.721</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,positive</td>\n",
              "                        \n",
              "                        <td>0.714</td><td>0.606</td><td>0.656</td><td>33</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#DESIGN&FEATURES,negative</td>\n",
              "                        \n",
              "                        <td>0.819</td><td>0.557</td><td>0.663</td><td>106</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#GENERAL,positive</td>\n",
              "                        <td rowspan=\"2\">0.667</td><td rowspan=\"2\">0.812</td><td rowspan=\"2\">0.732</td><td rowspan=\"2\">32</td>\n",
              "                        <td>0.714</td><td>0.806</td><td>0.758</td><td>31</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.250</td><td>1.000</td><td>0.400</td><td>1</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,positive</td>\n",
              "                        <td rowspan=\"2\">0.533</td><td rowspan=\"2\">0.407</td><td rowspan=\"2\">0.462</td><td rowspan=\"2\">59</td>\n",
              "                        <td>0.667</td><td>0.222</td><td>0.333</td><td>27</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>ROOM_AMENITIES#QUALITY,negative</td>\n",
              "                        \n",
              "                        <td>0.472</td><td>0.531</td><td>0.500</td><td>32</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,neutral</td>\n",
              "                        <td rowspan=\"3\">0.983</td><td rowspan=\"3\">0.978</td><td rowspan=\"3\">0.981</td><td rowspan=\"3\">416</td>\n",
              "                        <td>0.500</td><td>0.214</td><td>0.300</td><td>28</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.950</td><td>0.977</td><td>0.963</td><td>349</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,negative</td>\n",
              "                        \n",
              "                        <td>0.698</td><td>0.769</td><td>0.732</td><td>39</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>0.965258</td>\n",
              "      <td>0.985575</td>\n",
              "      <td>0.975310</td>\n",
              "      <td>17816.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>0.860355</td>\n",
              "      <td>0.805094</td>\n",
              "      <td>0.831808</td>\n",
              "      <td>1806.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>0.719836</td>\n",
              "      <td>0.545736</td>\n",
              "      <td>0.620811</td>\n",
              "      <td>645.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.120301</td>\n",
              "      <td>0.196319</td>\n",
              "      <td>133.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.950049</td>\n",
              "      <td>0.950049</td>\n",
              "      <td>0.950049</td>\n",
              "      <td>0.950049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.769696</td>\n",
              "      <td>0.614177</td>\n",
              "      <td>0.656062</td>\n",
              "      <td>20400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.945395</td>\n",
              "      <td>0.950049</td>\n",
              "      <td>0.946319</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>0.950</td>\n",
              "      <td>0.825</td>\n",
              "      <td>0.530</td>\n",
              "      <td>0.542</td>\n",
              "      <td>20400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aspect#Category</th>\n",
              "      <td>0.956</td>\n",
              "      <td>0.844</td>\n",
              "      <td>0.523</td>\n",
              "      <td>0.571</td>\n",
              "      <td>20400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Polarity</th>\n",
              "      <td>0.950</td>\n",
              "      <td>0.770</td>\n",
              "      <td>0.614</td>\n",
              "      <td>0.656</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, ASPECT_CATEGORY_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": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yt4As64AoFYz",
        "outputId": "8dbdee04-c3cd-4713-a768-1c434c5ee28f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "600it [00:00, 68337.11it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row mismatch:﻿#1 <-> #1\n",
            "Evaluation Result >> File:./predictions/Hotel-v1.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\t27\t414\t204\t111\t217\t34\t181\t64\t36\t100\t39\t113\t8\t39\t75\t29\t6\t63\t134\t30\t78\t17\t9\t45\t0\t5\t124\t0\t1\t4\t2\t0\t0\t0\n",
            "\n",
            "        Correct ANSWER: aspect\t16\t406\t187\t80\t211\t26\t166\t55\t31\t88\t26\t104\t7\t30\t63\t27\t4\t56\t120\t20\t60\t12\t7\t24\t0\t5\t115\t0\t1\t1\t2\t0\t0\t0\n",
            "             Precision: aspect\t0.5926\t0.9807\t0.9167\t0.7207\t0.9724\t0.7647\t0.9171\t0.8594\t0.8611\t0.8800\t0.6667\t0.9204\t0.8750\t0.7692\t0.8400\t0.9310\t0.6667\t0.8889\t0.8955\t0.6667\t0.7692\t0.7059\t0.7778\t0.5333\t0.0000\t1.0000\t0.9274\t0.0000\t1.0000\t0.2500\t1.0000\t0.0000\t0.0000\t0.0000\n",
            "                Recall: aspect\t0.3478\t0.9760\t0.9350\t0.8602\t0.9548\t0.4561\t0.8384\t0.8209\t0.4559\t0.6111\t0.8125\t0.8387\t0.7000\t0.4615\t0.7412\t0.5294\t0.3077\t0.7887\t0.7947\t0.6897\t0.6383\t0.5714\t0.1029\t0.4068\t0.0000\t0.1923\t0.8915\t0.0000\t0.0769\t0.1111\t0.4000\t0.0000\t0.0000\t0.0000\n",
            "              F1 score: aspect\t0.4384\t0.9783\t0.9257\t0.7843\t0.9635\t0.5714\t0.8760\t0.8397\t0.5962\t0.7213\t0.7324\t0.8776\t0.7778\t0.5769\t0.7875\t0.6750\t0.4211\t0.8358\t0.8421\t0.6780\t0.6977\t0.6316\t0.1818\t0.4615\t0.0000\t0.3226\t0.9091\t0.0000\t0.1429\t0.1538\t0.5714\t0.0000\t0.0000\t0.0000\n",
            "\n",
            "  Over All ANSWER: aspect:----\t0.8828\t0.7546\t0.8137\n",
            "\n",
            "  Correct ANSWER: aspect,value\t14\t376\t181\t76\t199\t23\t144\t55\t29\t79\t26\t99\t7\t22\t59\t25\t3\t52\t118\t18\t59\t11\t7\t23\t0\t4\t108\t0\t0\t1\t2\t0\t0\t0\n",
            "      Precision: aspect, value\t0.5185\t0.9082\t0.8873\t0.6847\t0.9171\t0.6765\t0.7956\t0.8594\t0.8056\t0.7900\t0.6667\t0.8761\t0.8750\t0.5641\t0.7867\t0.8621\t0.5000\t0.8254\t0.8806\t0.6000\t0.7564\t0.6471\t0.7778\t0.5111\t0.0000\t0.8000\t0.8710\t0.0000\t0.0000\t0.2500\t1.0000\t0.0000\t0.0000\t0.0000\n",
            "         Recall: aspect, value\t0.3043\t0.9038\t0.9050\t0.8172\t0.9005\t0.4035\t0.7273\t0.8209\t0.4265\t0.5486\t0.8125\t0.7984\t0.7000\t0.3385\t0.6941\t0.4902\t0.2308\t0.7324\t0.7815\t0.6207\t0.6277\t0.5238\t0.1029\t0.3898\t0.0000\t0.1538\t0.8372\t0.0000\t0.0000\t0.1111\t0.4000\t0.0000\t0.0000\t0.0000\n",
            "       F1 score: aspect, value\t0.3836\t0.9060\t0.8960\t0.7451\t0.9087\t0.5055\t0.7599\t0.8397\t0.5577\t0.6475\t0.7324\t0.8354\t0.7778\t0.4231\t0.7375\t0.6250\t0.3158\t0.7761\t0.8281\t0.6102\t0.6860\t0.5789\t0.1818\t0.4423\t0.0000\t0.2581\t0.8538\t0.0000\t0.0000\t0.1538\t0.5714\t0.0000\t0.0000\t0.0000\n",
            "\n",
            "Over All ANSWER: aspect, value:----\t0.8239\t0.7043\t0.7594\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, ASPECT_CATEGORY_NAMES)\n",
        "!java ../evaluators/SAEvaluate.java {TEST_PATH.replace('.csv', '.txt')} ./predictions/{MODEL_NAME}.txt"
      ]
    }
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