experiments/Restaurant-v2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmlWL-oDQ3Tw"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ds4v/absa-vlsp-2018/blob/main/experiments/Hotel-v1.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSyJrDH1WIJg",
        "outputId": "09b2556b-73c8-4ad5-aa13-27ee50c9db70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m431.4/431.4 kB\u001b[0m \u001b[31m8.6 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[31m28.7 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[31m13.6 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[31m44.2 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[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m10.7 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[31m28.3 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[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m8.9 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[31m7.1 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": "c73b5f3d-8faa-470d-de44-390d7e51c038"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'absa-vlsp-2018'...\n",
            "remote: Enumerating objects: 225, done.\u001b[K\n",
            "remote: Counting objects: 100% (136/136), done.\u001b[K\n",
            "remote: Compressing objects: 100% (96/96), done.\u001b[K\n",
            "remote: Total 225 (delta 61), reused 106 (delta 36), pack-reused 89\u001b[K\n",
            "Receiving objects: 100% (225/225), 31.72 MiB | 14.89 MiB/s, done.\n",
            "Resolving deltas: 100% (78/78), done.\n",
            "/content/absa-vlsp-2018/experiments\n",
            "Hotel-v1.ipynb\tHotel-v2.ipynb\tpredictions  Restaurant-v1.ipynb  Restaurant-v2.ipynb\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ds4v/absa-vlsp-2018.git\n",
        "%cd ./absa-vlsp-2018/experiments\n",
        "!mkdir predictions\n",
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ebq2fB17WIJh",
        "outputId": "35972e62-7493-4c2b-cca9-27b674c50295"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.15.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "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": "48c9ff0b-2f9b-481b-fcdd-3294bb30fb65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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_restaurant/1-VLSP2018-SA-Restaurant-train.csv'\n",
        "VAL_PATH = r'../datasets/vlsp2018_restaurant/2-VLSP2018-SA-Restaurant-dev.csv'\n",
        "TEST_PATH = r'../datasets/vlsp2018_restaurant/3-VLSP2018-SA-Restaurant-test.csv'\n",
        "PRETRAINED_MODEL = 'vinai/phobert-base'\n",
        "MODEL_NAME = 'Restaurant-v2' # Multitask model on Restaurant domain\n",
        "MAX_LENGTH = 256\n",
        "BATCH_SIZE = 21\n",
        "EPOCHS = 20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408,
          "referenced_widgets": [
            "5e901179d0fd422ea099e25625e3bde5",
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            "a25c441cbf8645c8a992bfa7e8d4cd5e",
            "df4eccbf4fb1418d9656e707c3f9ffde"
          ]
        },
        "id": "MtJDqdUUWIJk",
        "outputId": "b2a69d58-7e93-4549-ccf9-adb7b294b1e0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e901179d0fd422ea099e25625e3bde5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating val split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b714a8ffc86429591e7fe56479cd347"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "065f399bec2f4053afac67147445f524"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Review', 'AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL'],\n",
              "        num_rows: 2961\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['Review', 'AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL'],\n",
              "        num_rows: 1290\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Review', 'AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL'],\n",
              "        num_rows: 500\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "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": "eb3e077e-de68-4de4-ccc1-c4e3a58cdabf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VnCoreNLP word segmenter is loaded successfully.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/961 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb4b4d27894e4a3992a40b8bd6ea679a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.58G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89456bbd66b1498bae43aca974216011"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62e53b97581b4909bec1bb62563d8328"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/279 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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/",
          "height": 252,
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          ]
        },
        "id": "sgtRu6oMWIJm",
        "outputId": "917b54b5-98dc-40b8-fe16-065fd665df79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/557 [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/895k [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "bpe.codes:   0%|          | 0.00/1.14M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/3.13M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f31a3d81cb3d46c69a2281cb8dd3fc44"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_ids', 'token_type_ids', 'attention_mask']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL)\n",
        "tokens = tokenizer.encode('Tôi là sinh_viên trường đại_học Công_nghệ thông_tin .')\n",
        "print('Encode:', tokens, '\\nDecode:', tokenizer.decode(tokens))\n",
        "tokenizer.model_input_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "b32O1vDvWIJm",
        "outputId": "f33dd5a7-57bd-4cfc-8817-1a887fc8b76e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] Preprocessing and tokenizing text data...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "129704d183794646a396e7ff39279fd6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/2961 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "data": {
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            ]
          },
          "metadata": {},
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        },
        {
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              "model_id": "6facc26334e646a18e286c8f148267ae",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/2961 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "81e7be24565c41b1b8c418353d1abf1f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/1290 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8924be2d71c94dce9ba1355ac02057e9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/500 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 2961\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1290\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 500\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"})\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"raw_datasets\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"- Kimchi Kimchi \\ud83d\\ude39\\ud83d\\ude39- N\\u00f3i cho c\\u00f9ng th\\u00ec \\u0111\\u1ed3 \\u0103n c\\u00e0ng cay c\\u00e0ng ngon h\\u00e3 \\ud83d\\ude02- L\\u1ea9u b\\u00e1nh g\\u1ea1o ph\\u1ea7n 4 ng\\u01b0\\u1eddi \\u0103n \\u1edf kimchikimchi - ch\\u00fang ta c\\u00f3 th\\u1ec3 \\u0103n no n\\u00ea lu\\u00f4n - N\\u1ed3i l\\u1ea9u kh\\u00e1 to v\\u00e0 c\\u00f2n \\u0111\\u01b0\\u1ee3c t\\u1eb7ng k\\u00e8m 2 lon sting v\\u00e0 1 ph\\u1ea7n c\\u01a1m chi\\u00ean n\\u1eefa \\ud83d\\ude0d\\ud83d\\ude0d\\ud83d\\ude0d- \\u0110ang c\\u00f3 ch\\u01b0\\u01a1ng tr\\u00ecnh khuy\\u1ebfn m\\u1ea1i gi\\u1ea3m 50% cho ph\\u1ea7n l\\u1ea9u 4 ng\\u01b0\\u1eddi n\\u00e0y c\\u00e1c b\\u1ea1n \\u1ea1 - Ai m\\u00e0 nghi\\u1ec1n cay th\\u00ec kh\\u00f4ng th\\u1ec3 kh\\u00f4ng check in hhahaaa\",\n          \"_H\\u01b0\\u01a1ng v\\u1ecb th\\u01a1m ngon, \\u0103n cay cay r\\u1ea5t th\\u00edch, n\\u00eam n\\u1ebfm v\\u1eeba mi\\u1ec7ng. Ngo\\u00e0i ra menu qu\\u00e1n c\\u0169ng nhi\\u1ec1u m\\u00f3n kh\\u00e1c nhau tha h\\u1ed3 cho b\\u1ea1n l\\u1ef1a ch\\u1ecdn lu\\u00f4n._Qu\\u00e1n r\\u1ed9ng r\\u00e3i, view kh\\u00e1 \\u0111\\u1eb9p v\\u00e0 c\\u0169ng tho\\u00e1ng l\\u1eafm. Kh\\u00e1ch c\\u1ee7a qu\\u00e1n \\u0111\\u00f4ng n\\u00ean nhi\\u1ec1u khi nh\\u00e2n vi\\u00ean ph\\u1ee5c v\\u1ee5 kh\\u00f4ng \\u0111\\u01b0\\u1ee3c nhanh cho l\\u1eafm._Th\\u1ecbt heo r\\u1eebng gi\\u00e1 theo k\\u00ec n\\u00ean nh\\u1edb h\\u1ecfi tr\\u01b0\\u1edbc nh\\u01b0ng trung b\\u00ecnh l\\u00e0 150k 1 ph\\u1ea7n. Hai b\\u1ea1n \\u0111i chung chi kho\\u1ea3ng 400k h\\u01a1n l\\u00e0 \\u1ed5n nh\\u00e9.\",\n          \"- \\u0110\\u1ed3 n\\u01b0\\u1edbng th\\u00ec ch\\u1ed7 n\\u00e0y kh\\u00f4ng \\u00edt b\\u1ea1n \\\"ch\\u1ebb\\\" bi\\u1ebft \\u0111\\u1ebfn \\u0111\\u00e2u nh\\u1ec9 =))) - Gi\\u00e1 th\\u00ec c\\u1ef1c sinh vi\\u00ean lu\\u00f4n . Ch\\u1ec9 t\\u1eeb 5k 1 xi\\u00ean th\\u00f4i . 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ch\\u00fang_ta c\\u00f3_th\\u1ec3 \\u0103n no_n\\u00ea lu\\u00f4n n\\u1ed3i l\\u1ea9u kh\\u00e1 to v\\u00e0 c\\u00f2n \\u0111\\u01b0\\u1ee3c t\\u1eb7ng k\\u00e8m 2 lon sting v\\u00e0 1 ph\\u1ea7n c\\u01a1m_chi\\u00ean n\\u1eefa \\u0111ang c\\u00f3 ch\\u01b0\\u01a1ng_tr\\u00ecnh khuy\\u1ebfn_m\\u1ea1i gi\\u1ea3m 50 cho ph\\u1ea7n l\\u1ea9u 4 ng\\u01b0\\u1eddi n\\u00e0y c\\u00e1c b\\u1ea1n \\u1ea1 ai m\\u00e0 nghi\\u1ec1n cay th\\u00ec kh\\u00f4ng_th\\u1ec3 kh\\u00f4ng check in ha_ha ha </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> <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> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\",\n          \"<s> h\\u01b0\\u01a1ng_v\\u1ecb th\\u01a1m ngon_\\u0103n cay cay r\\u1ea5t th\\u00edch n\\u00eam n\\u1ebfm v\\u1eeba_mi\\u1ec7ng ngo\\u00e0i_ra menu qu\\u00e1n c\\u0169ng nhi\\u1ec1u m\\u00f3n kh\\u00e1c nhau tha_h\\u1ed3 cho b\\u1ea1n l\\u1ef1a_ch\\u1ecdn lu\\u00f4n qu\\u00e1n r\\u1ed9ng_r\\u00e3i view kh\\u00e1 \\u0111\\u1eb9p v\\u00e0 c\\u0169ng tho\\u00e1ng l\\u1eafm kh\\u00e1ch c\\u1ee7a qu\\u00e1n \\u0111\\u00f4ng n\\u00ean nhi\\u1ec1u khi nh\\u00e2n_vi\\u00ean ph\\u1ee5c_v\\u1ee5 kh\\u00f4ng \\u0111\\u01b0\\u1ee3c nhanh cho l\\u1eafm th\\u1ecbt heo r\\u1eebng gi\\u00e1 theo k\\u00ec n\\u00ean nh\\u1edb h\\u1ecfi tr\\u01b0\\u1edbc nh\\u01b0ng trung_b\\u00ecnh l\\u00e0 150k 1 ph\\u1ea7n hai b\\u1ea1n \\u0111i chung chi kho\\u1ea3ng 400k h\\u01a1n l\\u00e0 \\u1ed5n nh\\u00e9 </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> <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> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\",\n          \"<s> \\u0111\\u1ed3 n\\u01b0\\u1edbng th\\u00ec ch\\u1ed7 n\\u00e0y kh\\u00f4ng \\u00edt b\\u1ea1n ch\\u00ea bi\\u1ebft \\u0111\\u1ebfn \\u0111\\u00e2u nh\\u1ec9 gi\\u00e1 th\\u00ec c\\u1ef1c sinh_vi\\u00ean lu\\u00f4n ch\\u1ec9 t\\u1eeb 5k 1 xi\\u00ean th\\u00f4i c\\u00f3 c\\u1ea3 b\\u00f2 ph\\u00f4_mai v\\u1edbi c\\u1ea3 b\\u1ea1ch_tu\\u1ed9c n\\u01b0\\u1edbng n\\u1eefa th\\u1ecbt \\u1edf \\u0111\\u00e2y \\u01b0\\u1edbp r\\u1ea5t l\\u00e0 ngon v\\u1eeba_mi\\u1ec7ng \\u0103n n\\u1eefa n\\u00f3i_chung l\\u00e0 kh\\u00f4ng c\\u00f3 ch\\u1ed7 ch\\u00ea h\\u00ea h\\u00ea </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> <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> <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\": \"\"\n      }\n    }\n  ]\n}",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>raw_datasets</th>\n",
              "      <th>encoded_input_ids</th>\n",
              "      <th>decoded_input_ids</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>_ Ảnh chụp từ hôm qua, đi chơi với gia đình và...</td>\n",
              "      <td>[0, 284, 690, 39, 2402, 57, 379, 15, 161, 6, 9...</td>\n",
              "      <td>&lt;s&gt; ảnh chụp từ hôm_qua đi chơi với gia_đình v...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>_Hương vị thơm ngon, ăn cay cay rất thích, nêm...</td>\n",
              "      <td>[0, 4088, 2023, 21474, 4205, 4205, 59, 543, 61...</td>\n",
              "      <td>&lt;s&gt; hương_vị thơm ngon_ăn cay cay rất thích nê...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>- 1 bàn tiệc hoành tráng 3 đứa ăn no muốn tắt ...</td>\n",
              "      <td>[0, 99, 509, 2418, 4600, 107, 802, 203, 4397, ...</td>\n",
              "      <td>&lt;s&gt; 1 bàn tiệc hoành_tráng 3 đứa ăn no muốn tắ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>- Các bạn nhìn cái chảo này có to không 🙄🙄🙄- T...</td>\n",
              "      <td>[0, 9, 88, 364, 224, 5596, 23, 10, 889, 17, 34...</td>\n",
              "      <td>&lt;s&gt; các bạn nhìn cái chảo này có to không trên...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>- Cháo: có nhiều hương cho các bạn chọn, nhưng...</td>\n",
              "      <td>[0, 4951, 10, 36, 2263, 13, 9, 88, 437, 51, 68...</td>\n",
              "      <td>&lt;s&gt; cháo có nhiều hương cho các bạn chọn nhưng...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>- Đồ nướng thì chỗ này không ít bạn \"chẻ\" biết...</td>\n",
              "      <td>[0, 779, 3030, 54, 683, 23, 17, 343, 88, 4241,...</td>\n",
              "      <td>&lt;s&gt; đồ nướng thì chỗ này không ít bạn chê biết...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>- Đói không ngủ được nên up hình đồ ăn cho mấy...</td>\n",
              "      <td>[0, 3442, 17, 845, 11, 77, 7249, 819, 4036, 13...</td>\n",
              "      <td>&lt;s&gt; đói không ngủ được nên up hình đồ_ăn cho m...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>- Khẩu vị vừa ăn hợp vệ sinh , không gian quán...</td>\n",
              "      <td>[0, 13284, 164, 203, 2288, 1540, 1152, 946, 72...</td>\n",
              "      <td>&lt;s&gt; khẩu_vị vừa ăn hợp vệ_sinh không_gian quán...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>- Kimchi Kimchi 😹😹- Nói cho cùng thì đồ ăn càn...</td>\n",
              "      <td>[0, 39010, 811, 39010, 811, 96, 13, 81, 54, 40...</td>\n",
              "      <td>&lt;s&gt; kimchi kimchi nói cho cùng thì đồ_ăn càng ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>- Lẩu hàu thì chưa thấy đâu có. Mới ăn lần đầu...</td>\n",
              "      <td>[0, 8510, 15389, 54, 102, 108, 602, 10, 60, 20...</td>\n",
              "      <td>&lt;s&gt; lẩu hàu thì chưa thấy đâu có mới ăn lần đầ...</td>\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-716cad94-d4a0-4320-a59c-69592cb5fc6b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                        raw_datasets  \\\n",
              "0  _ Ảnh chụp từ hôm qua, đi chơi với gia đình và...   \n",
              "1  _Hương vị thơm ngon, ăn cay cay rất thích, nêm...   \n",
              "2  - 1 bàn tiệc hoành tráng 3 đứa ăn no muốn tắt ...   \n",
              "3  - Các bạn nhìn cái chảo này có to không 🙄🙄🙄- T...   \n",
              "4  - Cháo: có nhiều hương cho các bạn chọn, nhưng...   \n",
              "5  - Đồ nướng thì chỗ này không ít bạn \"chẻ\" biết...   \n",
              "6  - Đói không ngủ được nên up hình đồ ăn cho mấy...   \n",
              "7  - Khẩu vị vừa ăn hợp vệ sinh , không gian quán...   \n",
              "8  - Kimchi Kimchi 😹😹- Nói cho cùng thì đồ ăn càn...   \n",
              "9  - Lẩu hàu thì chưa thấy đâu có. Mới ăn lần đầu...   \n",
              "\n",
              "                                   encoded_input_ids  \\\n",
              "0  [0, 284, 690, 39, 2402, 57, 379, 15, 161, 6, 9...   \n",
              "1  [0, 4088, 2023, 21474, 4205, 4205, 59, 543, 61...   \n",
              "2  [0, 99, 509, 2418, 4600, 107, 802, 203, 4397, ...   \n",
              "3  [0, 9, 88, 364, 224, 5596, 23, 10, 889, 17, 34...   \n",
              "4  [0, 4951, 10, 36, 2263, 13, 9, 88, 437, 51, 68...   \n",
              "5  [0, 779, 3030, 54, 683, 23, 17, 343, 88, 4241,...   \n",
              "6  [0, 3442, 17, 845, 11, 77, 7249, 819, 4036, 13...   \n",
              "7  [0, 13284, 164, 203, 2288, 1540, 1152, 946, 72...   \n",
              "8  [0, 39010, 811, 39010, 811, 96, 13, 81, 54, 40...   \n",
              "9  [0, 8510, 15389, 54, 102, 108, 602, 10, 60, 20...   \n",
              "\n",
              "                                   decoded_input_ids  \n",
              "0  <s> ảnh chụp từ hôm_qua đi chơi với gia_đình v...  \n",
              "1  <s> hương_vị thơm ngon_ăn cay cay rất thích nê...  \n",
              "2  <s> 1 bàn tiệc hoành_tráng 3 đứa ăn no muốn tắ...  \n",
              "3  <s> các bạn nhìn cái chảo này có to không trên...  \n",
              "4  <s> cháo có nhiều hương cho các bạn chọn nhưng...  \n",
              "5  <s> đồ nướng thì chỗ này không ít bạn chê biết...  \n",
              "6  <s> đói không ngủ được nên up hình đồ_ăn cho m...  \n",
              "7  <s> khẩu_vị vừa ăn hợp vệ_sinh không_gian quán...  \n",
              "8  <s> kimchi kimchi nói cho cùng thì đồ_ăn càng ...  \n",
              "9  <s> lẩu hàu thì chưa thấy đâu có mới ăn lần đầ...  "
            ]
          },
          "execution_count": 20,
          "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_restaurant')\n",
        "# display(preprocessed_datasets)\n",
        "# pd.DataFrame({\n",
        "#     'raw_datasets': raw_datasets['train']['Review'][:10],\n",
        "#     'encoded_input_ids': preprocessed_datasets['train']['input_ids'][:10],\n",
        "#     'decoded_input_ids': [tokenizer.decode(preprocessed_datasets['train'][i]['input_ids']) for i in range(10)]\n",
        "# })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSM6s-J4WIJn",
        "outputId": "fb526a31-03ba-49d2-fc57-22d7f07b8191"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 2961\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1290\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['AMBIENCE#GENERAL', 'DRINKS#PRICES', 'DRINKS#QUALITY', 'DRINKS#STYLE&OPTIONS', 'FOOD#PRICES', 'FOOD#QUALITY', 'FOOD#STYLE&OPTIONS', 'LOCATION#GENERAL', 'RESTAURANT#GENERAL', 'RESTAURANT#MISCELLANEOUS', 'RESTAURANT#PRICES', 'SERVICE#GENERAL', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 500\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "from datasets import load_from_disk\n",
        "preprocessed_datasets = load_from_disk('../datasets/preprocessed_restaurant')\n",
        "preprocessed_datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGRUGqTWWIJn"
      },
      "source": [
        "# Prepare for TensorFlow Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCWAdAozDcAE",
        "outputId": "02953be6-587c-4772-98d4-42259e7dc3e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'AMBIENCE#GENERAL': 'output_1', 'DRINKS#PRICES': 'output_2', 'DRINKS#QUALITY': 'output_3', 'DRINKS#STYLE&OPTIONS': 'output_4', 'FOOD#PRICES': 'output_5', 'FOOD#QUALITY': 'output_6', 'FOOD#STYLE&OPTIONS': 'output_7', 'LOCATION#GENERAL': 'output_8', 'RESTAURANT#GENERAL': 'output_9', 'RESTAURANT#MISCELLANEOUS': 'output_10', 'RESTAURANT#PRICES': 'output_11', 'SERVICE#GENERAL': 'output_12'}\n"
          ]
        }
      ],
      "source": [
        "# Tensorflow only accepts targeted column names prefixed with 'output_'\n",
        "MULTI_BRANCH_NAMES = {old_name: f'output_{i + 1}' for i, old_name in enumerate(raw_datasets['train'].column_names[1:])}\n",
        "preprocessed_datasets = preprocessed_datasets.rename_columns(MULTI_BRANCH_NAMES)\n",
        "steps_per_epoch = len(preprocessed_datasets['train']) // BATCH_SIZE\n",
        "total_steps = EPOCHS * steps_per_epoch\n",
        "print(MULTI_BRANCH_NAMES)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "AWlh8K7OWIJn"
      },
      "outputs": [],
      "source": [
        "train_tf_dataset = preprocessed_datasets['train'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols=list(MULTI_BRANCH_NAMES.values()),\n",
        "    batch_size=BATCH_SIZE, shuffle=True, num_workers=8\n",
        ")\n",
        "val_tf_dataset = preprocessed_datasets['val'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols=list(MULTI_BRANCH_NAMES.values()),\n",
        "    batch_size=BATCH_SIZE, shuffle=False, num_workers=8\n",
        ")\n",
        "test_tf_dataset = preprocessed_datasets['test'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols=list(MULTI_BRANCH_NAMES.values()),\n",
        "    batch_size=BATCH_SIZE, shuffle=False, num_workers=8\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "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 can 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": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 983
        },
        "id": "Ots6QvfKNS9E",
        "outputId": "cc8af172-514b-437b-ba11-6af93fd57e0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model_7/roberta/pooler/dense/kernel:0', 'tf_roberta_model_7/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_7/roberta/pooler/dense/kernel:0', 'tf_roberta_model_7/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_7/roberta/pooler/dense/kernel:0', 'tf_roberta_model_7/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_7/roberta/pooler/dense/kernel:0', 'tf_roberta_model_7/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "141/141 [==============================] - 124s 467ms/step - loss: 5.8219 - output_1_loss: 0.5350 - output_2_loss: 0.1888 - output_3_loss: 0.2088 - output_4_loss: 0.1725 - output_5_loss: 0.9143 - output_6_loss: 0.6993 - output_7_loss: 0.8556 - output_8_loss: 0.4536 - output_9_loss: 0.6514 - output_10_loss: 0.2471 - output_11_loss: 0.3712 - output_12_loss: 0.5243 - val_loss: 4.2244 - val_output_1_loss: 0.3081 - val_output_2_loss: 0.1704 - val_output_3_loss: 0.1569 - val_output_4_loss: 0.0481 - val_output_5_loss: 0.5518 - val_output_6_loss: 0.5413 - val_output_7_loss: 0.9595 - val_output_8_loss: 0.2458 - val_output_9_loss: 0.4658 - val_output_10_loss: 0.3889 - val_output_11_loss: 0.1408 - val_output_12_loss: 0.2472\n",
            "Epoch 2/20\n",
            "141/141 [==============================] - 56s 363ms/step - loss: 3.8286 - output_1_loss: 0.2824 - output_2_loss: 0.1334 - output_3_loss: 0.1558 - output_4_loss: 0.1229 - output_5_loss: 0.5372 - output_6_loss: 0.4592 - output_7_loss: 0.6357 - output_8_loss: 0.2669 - output_9_loss: 0.4867 - output_10_loss: 0.1933 - output_11_loss: 0.3111 - output_12_loss: 0.2438 - val_loss: 3.9012 - val_output_1_loss: 0.2303 - val_output_2_loss: 0.1299 - val_output_3_loss: 0.1038 - val_output_4_loss: 0.0777 - val_output_5_loss: 0.4996 - val_output_6_loss: 0.4704 - val_output_7_loss: 1.0008 - val_output_8_loss: 0.2762 - val_output_9_loss: 0.4533 - val_output_10_loss: 0.3664 - val_output_11_loss: 0.1356 - val_output_12_loss: 0.1573\n",
            "Epoch 3/20\n",
            "141/141 [==============================] - 54s 352ms/step - loss: 2.8660 - output_1_loss: 0.2030 - output_2_loss: 0.1102 - output_3_loss: 0.1075 - output_4_loss: 0.0905 - output_5_loss: 0.4335 - output_6_loss: 0.3005 - output_7_loss: 0.4877 - output_8_loss: 0.1769 - output_9_loss: 0.3653 - output_10_loss: 0.1540 - output_11_loss: 0.2694 - output_12_loss: 0.1675 - val_loss: 3.9351 - val_output_1_loss: 0.2433 - val_output_2_loss: 0.1017 - val_output_3_loss: 0.1082 - val_output_4_loss: 0.0433 - val_output_5_loss: 0.5413 - val_output_6_loss: 0.4775 - val_output_7_loss: 0.9389 - val_output_8_loss: 0.3005 - val_output_9_loss: 0.4858 - val_output_10_loss: 0.3464 - val_output_11_loss: 0.1908 - val_output_12_loss: 0.1572\n",
            "Epoch 4/20\n",
            "141/141 [==============================] - 55s 353ms/step - loss: 2.1099 - output_1_loss: 0.1442 - output_2_loss: 0.0892 - output_3_loss: 0.0814 - output_4_loss: 0.0705 - output_5_loss: 0.3124 - output_6_loss: 0.2153 - output_7_loss: 0.3343 - output_8_loss: 0.1371 - output_9_loss: 0.2600 - output_10_loss: 0.1142 - output_11_loss: 0.2224 - output_12_loss: 0.1289 - val_loss: 3.8276 - val_output_1_loss: 0.2649 - val_output_2_loss: 0.1074 - val_output_3_loss: 0.1111 - val_output_4_loss: 0.0447 - val_output_5_loss: 0.5258 - val_output_6_loss: 0.4691 - val_output_7_loss: 0.8551 - val_output_8_loss: 0.2578 - val_output_9_loss: 0.5261 - val_output_10_loss: 0.3446 - val_output_11_loss: 0.1227 - val_output_12_loss: 0.1984\n",
            "Epoch 5/20\n",
            "141/141 [==============================] - 54s 353ms/step - loss: 1.5624 - output_1_loss: 0.1125 - output_2_loss: 0.0699 - output_3_loss: 0.0590 - output_4_loss: 0.0618 - output_5_loss: 0.2401 - output_6_loss: 0.1639 - output_7_loss: 0.2442 - output_8_loss: 0.0862 - output_9_loss: 0.1719 - output_10_loss: 0.0834 - output_11_loss: 0.1847 - output_12_loss: 0.0848 - val_loss: 4.3340 - val_output_1_loss: 0.2174 - val_output_2_loss: 0.1107 - val_output_3_loss: 0.1238 - val_output_4_loss: 0.0537 - val_output_5_loss: 0.5750 - val_output_6_loss: 0.4522 - val_output_7_loss: 1.0911 - val_output_8_loss: 0.2548 - val_output_9_loss: 0.7415 - val_output_10_loss: 0.3478 - val_output_11_loss: 0.2108 - val_output_12_loss: 0.1553\n",
            "Epoch 6/20\n",
            "141/141 [==============================] - 54s 353ms/step - loss: 0.8061 - output_1_loss: 0.0620 - output_2_loss: 0.0487 - output_3_loss: 0.0505 - output_4_loss: 0.0400 - output_5_loss: 0.1040 - output_6_loss: 0.0655 - output_7_loss: 0.1302 - output_8_loss: 0.0400 - output_9_loss: 0.0894 - output_10_loss: 0.0399 - output_11_loss: 0.0855 - output_12_loss: 0.0504 - val_loss: 5.2321 - val_output_1_loss: 0.2687 - val_output_2_loss: 0.0891 - val_output_3_loss: 0.1185 - val_output_4_loss: 0.0498 - val_output_5_loss: 0.5700 - val_output_6_loss: 0.5170 - val_output_7_loss: 1.7492 - val_output_8_loss: 0.2690 - val_output_9_loss: 0.8810 - val_output_10_loss: 0.4121 - val_output_11_loss: 0.1378 - val_output_12_loss: 0.1700\n",
            "Epoch 7/20\n",
            "141/141 [==============================] - ETA: 0s - loss: 0.3874 - output_1_loss: 0.0333 - output_2_loss: 0.0297 - output_3_loss: 0.0251 - output_4_loss: 0.0266 - output_5_loss: 0.0506 - output_6_loss: 0.0326 - output_7_loss: 0.0502 - output_8_loss: 0.0126 - output_9_loss: 0.0310 - output_10_loss: 0.0255 - output_11_loss: 0.0404 - output_12_loss: 0.0299Restoring model weights from the end of the best epoch: 4.\n",
            "141/141 [==============================] - 55s 350ms/step - loss: 0.3874 - output_1_loss: 0.0333 - output_2_loss: 0.0297 - output_3_loss: 0.0251 - output_4_loss: 0.0266 - output_5_loss: 0.0506 - output_6_loss: 0.0326 - output_7_loss: 0.0502 - output_8_loss: 0.0126 - output_9_loss: 0.0310 - output_10_loss: 0.0255 - output_11_loss: 0.0404 - output_12_loss: 0.0299 - val_loss: 5.0290 - val_output_1_loss: 0.2884 - val_output_2_loss: 0.0903 - val_output_3_loss: 0.1229 - val_output_4_loss: 0.0542 - val_output_5_loss: 0.6013 - val_output_6_loss: 0.5500 - val_output_7_loss: 1.4294 - val_output_8_loss: 0.2948 - val_output_9_loss: 0.8596 - val_output_10_loss: 0.3704 - val_output_11_loss: 0.1940 - val_output_12_loss: 0.1737\n",
            "Epoch 7: early stopping\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 5min 1s, sys: 42.3 s, total: 5min 44s\n",
            "Wall time: 7min 36s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "from acsa_model import VLSP2018MultiTask\n",
        "from helper import plot_training_history\n",
        "model = VLSP2018MultiTask(PRETRAINED_MODEL, MULTI_BRANCH_NAMES, optimizer, multi_branch=True, name=MODEL_NAME)\n",
        "\n",
        "history = model.fit(\n",
        "    train_tf_dataset,\n",
        "    validation_data = val_tf_dataset,\n",
        "    callbacks = [early_stop_callback],\n",
        "    epochs = EPOCHS,\n",
        "    verbose = 1\n",
        ").history\n",
        "\n",
        "model.save_weights(f'./weights/{MODEL_NAME}/{MODEL_NAME}', save_format='tf')\n",
        "plot_training_history(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bekGxelJmM6g",
        "outputId": "5b250a62-7c89-4012-9ca7-e25ba6f6474f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/GoogleDrive\n",
            "  adding: weights/Restaurant-v2/ (stored 0%)\n",
            "  adding: weights/Restaurant-v2/checkpoint (deflated 39%)\n",
            "  adding: weights/Restaurant-v2/Restaurant-v2.index (deflated 80%)\n",
            "  adding: weights/Restaurant-v2/Restaurant-v2.data-00000-of-00001 (deflated 32%)\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive # Move weights to Drive for faster downloading\n",
        "drive.mount('/content/GoogleDrive', force_remount=True)\n",
        "!zip -r ./weights/{MODEL_NAME}.zip ./weights/{MODEL_NAME}\n",
        "!mv ./weights/{MODEL_NAME}.zip /content/GoogleDrive/My\\ Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELoAR9-Y9kSF"
      },
      "source": [
        "# Predict on the Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkqO1rs0Qrw0",
        "outputId": "b7167c39-83c3-4435-d343-e98f3b9cce5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "62/62 [==============================] - 30s 113ms/step - loss: 3.8276 - output_1_loss: 0.2649 - output_2_loss: 0.1074 - output_3_loss: 0.1111 - output_4_loss: 0.0447 - output_5_loss: 0.5258 - output_6_loss: 0.4691 - output_7_loss: 0.8551 - output_8_loss: 0.2578 - output_9_loss: 0.5261 - output_10_loss: 0.3446 - output_11_loss: 0.1227 - output_12_loss: 0.1984\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.8275723457336426,\n",
              " 0.26491811871528625,\n",
              " 0.10736430436372757,\n",
              " 0.11110882461071014,\n",
              " 0.044701945036649704,\n",
              " 0.5257941484451294,\n",
              " 0.4690759479999542,\n",
              " 0.855054497718811,\n",
              " 0.25782346725463867,\n",
              " 0.5260519981384277,\n",
              " 0.34458303451538086,\n",
              " 0.12273108959197998,\n",
              " 0.19836458563804626]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "from acsa_model import VLSP2018MultiTask\n",
        "reloaded_model = VLSP2018MultiTask(PRETRAINED_MODEL, MULTI_BRANCH_NAMES, optimizer, multi_branch=True, name=MODEL_NAME)\n",
        "reloaded_model.load_weights(f'./weights/{MODEL_NAME}/{MODEL_NAME}') # Reload the Model\n",
        "reloaded_model.evaluate(val_tf_dataset, batch_size=BATCH_SIZE, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuW9rBsnWIJo",
        "outputId": "5236c13a-bb9f-4dc8-b9bc-5c3dd8ce6b84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24/24 [==============================] - 16s 143ms/step\n",
            "24/24 [==============================] - 8s 103ms/step - loss: 7.7871 - output_1_loss: 0.7802 - output_2_loss: 0.6175 - output_3_loss: 0.3296 - output_4_loss: 0.2471 - output_5_loss: 0.8136 - output_6_loss: 0.4632 - output_7_loss: 0.8820 - output_8_loss: 0.5582 - output_9_loss: 0.8387 - output_10_loss: 0.9830 - output_11_loss: 0.4389 - output_12_loss: 0.8352\n",
            "Example: Đây là 1 trong những quán mà mình thích vì vị trà đậm và thơm cũng như mùi vị đặc trưng hơn hẳn những quán khác nè  Trà sữa trân châu sợi - 46k Trà sữa pha khá ngon, vị trà chát và mùi hương khá rõ, không quá ngọt, rất đúng với gu mình  Trà đào - 45k Vị trà đào ở đây cũng đặc biệt hơn hẳn những quán khác, không phải chua ngọt như thưởng thấy mà có mùi trà rất ngon  Cà phê đá xay - 65k Món đá xay ở đây uống cũng ngon không kém trà nè, mùi vị thơm hương cà phê, vị đắng kết hợp hoàn hảo với độ béo ngọt của whipping cream, không quá đắng, cũng không quá ngọt hay lạt lẽo mà dịu nhẹ, thơm và dễ uống lắm  Trà vải thiết quan âm - 45k Trà vải có mùi vị rất thơm ngon mùi vải mà vẫn nghe rõ vị trà, có chút vị chát nhẹ mùi trà thơm rất thích, không phải chỉ toàn vị syrup vải ngọt gắt như nhiều chỗ khác. Do trà ở đây pha khá đậm nên bạn nào uống mà đang đói sẽ dễ say nha, hoặc ban đêm có thể khó ngủ à, cảnh báo trước  Trà thiết quan âm latte - 47k Ly này thì vị trà rất đậm nên cảm giác hơi nhạt và chát, phần kem sữa bên trên béo béo uống chung thì vừa  Trà sữa 101 - 52k Ly này chụp ké chứ không có thử :v\n",
            "=> DRINKS#QUALITY,positive\n",
            "=> DRINKS#STYLE&OPTIONS,positive\n",
            "=> FOOD#STYLE&OPTIONS,positive\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": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2C-dHffWIJp",
        "outputId": "03e7b42c-d910-4b03-f7d1-1f7f7adadbb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter your review: Giá cũng ok mà nhân viên hơi thiếu chuyên nghiệp\n",
            "[INFO] Preprocessing and tokenizing text data...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 7s 7s/step\n",
            "=> FOOD#PRICES,positive\n",
            "=> FOOD#QUALITY,positive\n",
            "=> SERVICE#GENERAL,negative\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": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFTFgzTNWIJp",
        "outputId": "2c07d1dd-09e1-47ec-bdd0-ba597c0102e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 3, 1, ..., 0, 0, 0],\n",
              "       [0, 0, 1, ..., 3, 0, 0],\n",
              "       [1, 1, 1, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 1],\n",
              "       [1, 0, 1, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "y_test = preprocessed_datasets['test'].remove_columns(tokenizer.model_input_names)\n",
        "y_test = y_test.to_pandas().to_numpy()\n",
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 848
        },
        "id": "u_6eV6CGWIJp",
        "outputId": "b24b2a8b-5263-47fa-8c9d-40f898958ef2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 15/15 [00:00<00:00, 43996.20it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "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>AMBIENCE#GENERAL,negative</td>\n",
              "                        <td rowspan=\"3\">0.917</td><td rowspan=\"3\">0.776</td><td rowspan=\"3\">0.841</td><td rowspan=\"3\">255</td>\n",
              "                        <td>0.750</td><td>0.115</td><td>0.200</td><td>26</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>AMBIENCE#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.782</td><td>0.834</td><td>0.807</td><td>181</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>AMBIENCE#GENERAL,neutral</td>\n",
              "                        \n",
              "                        <td>0.421</td><td>0.167</td><td>0.239</td><td>48</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#PRICES,negative</td>\n",
              "                        <td rowspan=\"3\">1.000</td><td rowspan=\"3\">0.013</td><td rowspan=\"3\">0.026</td><td rowspan=\"3\">76</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>3</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#PRICES,positive</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>28</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#PRICES,neutral</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.022</td><td>0.043</td><td>45</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#QUALITY,negative</td>\n",
              "                        <td rowspan=\"3\">0.881</td><td rowspan=\"3\">0.521</td><td rowspan=\"3\">0.655</td><td rowspan=\"3\">71</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>6</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>0.667</td><td>0.519</td><td>0.583</td><td>54</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#QUALITY,neutral</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>11</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#STYLE&OPTIONS,negative</td>\n",
              "                        <td rowspan=\"3\">0.680</td><td rowspan=\"3\">0.370</td><td rowspan=\"3\">0.479</td><td rowspan=\"3\">46</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>1</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#STYLE&OPTIONS,positive</td>\n",
              "                        \n",
              "                        <td>0.560</td><td>0.341</td><td>0.424</td><td>41</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#STYLE&OPTIONS,neutral</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>4</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#PRICES,negative</td>\n",
              "                        <td rowspan=\"3\">0.848</td><td rowspan=\"3\">0.931</td><td rowspan=\"3\">0.888</td><td rowspan=\"3\">331</td>\n",
              "                        <td>0.500</td><td>0.214</td><td>0.300</td><td>28</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#PRICES,positive</td>\n",
              "                        \n",
              "                        <td>0.659</td><td>0.695</td><td>0.677</td><td>128</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#PRICES,neutral</td>\n",
              "                        \n",
              "                        <td>0.676</td><td>0.834</td><td>0.747</td><td>175</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#QUALITY,negative</td>\n",
              "                        <td rowspan=\"3\">0.963</td><td rowspan=\"3\">0.956</td><td rowspan=\"3\">0.959</td><td rowspan=\"3\">457</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>11</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>0.893</td><td>0.953</td><td>0.922</td><td>403</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#QUALITY,neutral</td>\n",
              "                        \n",
              "                        <td>0.625</td><td>0.349</td><td>0.448</td><td>43</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#STYLE&OPTIONS,negative</td>\n",
              "                        <td rowspan=\"3\">0.909</td><td rowspan=\"3\">0.839</td><td rowspan=\"3\">0.872</td><td rowspan=\"3\">403</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>16</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#STYLE&OPTIONS,positive</td>\n",
              "                        \n",
              "                        <td>0.792</td><td>0.880</td><td>0.834</td><td>334</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#STYLE&OPTIONS,neutral</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.019</td><td>0.037</td><td>53</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,negative</td>\n",
              "                        <td rowspan=\"3\">0.950</td><td rowspan=\"3\">0.631</td><td rowspan=\"3\">0.758</td><td rowspan=\"3\">179</td>\n",
              "                        <td>0.524</td><td>0.688</td><td>0.595</td><td>16</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.672</td><td>0.641</td><td>0.656</td><td>64</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,neutral</td>\n",
              "                        \n",
              "                        <td>0.865</td><td>0.323</td><td>0.471</td><td>99</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#GENERAL,negative</td>\n",
              "                        <td rowspan=\"3\">0.629</td><td rowspan=\"3\">0.722</td><td rowspan=\"3\">0.672</td><td rowspan=\"3\">223</td>\n",
              "                        <td>1.000</td><td>0.077</td><td>0.143</td><td>13</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.592</td><td>0.737</td><td>0.657</td><td>205</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#GENERAL,neutral</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>RESTAURANT#MISCELLANEOUS,negative</td>\n",
              "                        <td rowspan=\"3\">0.833</td><td rowspan=\"3\">0.077</td><td rowspan=\"3\">0.141</td><td rowspan=\"3\">130</td>\n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>9</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#MISCELLANEOUS,positive</td>\n",
              "                        \n",
              "                        <td>0.875</td><td>0.119</td><td>0.209</td><td>59</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#MISCELLANEOUS,neutral</td>\n",
              "                        \n",
              "                        <td>0.667</td><td>0.032</td><td>0.062</td><td>62</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#PRICES,negative</td>\n",
              "                        <td rowspan=\"3\">0.250</td><td rowspan=\"3\">0.041</td><td rowspan=\"3\">0.071</td><td rowspan=\"3\">73</td>\n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>5</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#PRICES,positive</td>\n",
              "                        \n",
              "                        <td>0.200</td><td>0.045</td><td>0.074</td><td>44</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#PRICES,neutral</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.042</td><td>0.080</td><td>24</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,negative</td>\n",
              "                        <td rowspan=\"3\">0.991</td><td rowspan=\"3\">0.617</td><td rowspan=\"3\">0.761</td><td rowspan=\"3\">175</td>\n",
              "                        <td>0.640</td><td>0.640</td><td>0.640</td><td>25</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.963</td><td>0.617</td><td>0.752</td><td>128</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,neutral</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.091</td><td>0.167</td><td>22</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>0.828813</td>\n",
              "      <td>0.930187</td>\n",
              "      <td>0.876579</td>\n",
              "      <td>3581.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>0.769231</td>\n",
              "      <td>0.742960</td>\n",
              "      <td>0.755867</td>\n",
              "      <td>1669.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>0.569231</td>\n",
              "      <td>0.232704</td>\n",
              "      <td>0.330357</td>\n",
              "      <td>159.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.351946</td>\n",
              "      <td>0.464804</td>\n",
              "      <td>591.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.802667</td>\n",
              "      <td>0.802667</td>\n",
              "      <td>0.802667</td>\n",
              "      <td>0.802667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.712871</td>\n",
              "      <td>0.564449</td>\n",
              "      <td>0.606902</td>\n",
              "      <td>6000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.791117</td>\n",
              "      <td>0.802667</td>\n",
              "      <td>0.787966</td>\n",
              "      <td>6000.000000</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>0.803</td>\n",
              "      <td>0.765</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.430</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aspect#Category</th>\n",
              "      <td>0.844</td>\n",
              "      <td>0.821</td>\n",
              "      <td>0.571</td>\n",
              "      <td>0.615</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Polarity</th>\n",
              "      <td>0.803</td>\n",
              "      <td>0.713</td>\n",
              "      <td>0.564</td>\n",
              "      <td>0.607</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "                </div>\n",
              "            </div>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from evaluators.sklearn_evaluator import VLSP2018SklearnEvaluator\n",
        "sk_eval = VLSP2018SklearnEvaluator(y_test, y_pred, list(MULTI_BRANCH_NAMES))\n",
        "# sk_eval.report(report_type='Aspect#Category,Polarity').round(3)\n",
        "# sk_eval.report(report_type='Aspect#Category').round(3)\n",
        "# sk_eval.report(report_type='Polarity').round(3)\n",
        "# sk_eval.report(report_type='macro_avg').round(3)\n",
        "sk_eval.report(report_type='all')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLhItgRyWIJp"
      },
      "source": [
        "# VLSP Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yt4As64AoFYz",
        "outputId": "e3d72220-4340-49c9-b2b4-00178e74377d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "500it [00:00, 78421.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row mismatch:﻿#1 <-> #1\n",
            "Evaluation Result >> File:./predictions/Restaurant-v2.txt<> [../datasets/vlsp2018_restaurant/3-VLSP2018-SA-Restaurant-test.txt]\n",
            "                    Gold count\t223\t71\t76\t46\t179\t130\t403\t331\t457\t255\t73\t175\n",
            "                  ANSWER count\t256\t42\t1\t25\t119\t12\t372\t363\t454\t216\t12\t109\n",
            "\n",
            "        Correct ANSWER: aspect\t161\t36\t1\t16\t113\t10\t338\t308\t437\t198\t3\t108\n",
            "             Precision: aspect\t0.6289\t0.8571\t1.0000\t0.6400\t0.9496\t0.8333\t0.9086\t0.8485\t0.9626\t0.9167\t0.2500\t0.9908\n",
            "                Recall: aspect\t0.7220\t0.5070\t0.0132\t0.3478\t0.6313\t0.0769\t0.8387\t0.9305\t0.9562\t0.7765\t0.0411\t0.6171\n",
            "              F1 score: aspect\t0.6722\t0.6372\t0.0260\t0.4507\t0.7584\t0.1408\t0.8723\t0.8876\t0.9594\t0.8408\t0.0706\t0.7606\n",
            "\n",
            "  Over All ANSWER: aspect:----\t0.8728\t0.7148\t0.7859\n",
            "\n",
            "  Correct ANSWER: aspect,value\t152\t27\t1\t14\t84\t9\t295\t241\t399\t162\t3\t97\n",
            "      Precision: aspect, value\t0.5938\t0.6429\t1.0000\t0.5600\t0.7059\t0.7500\t0.7930\t0.6639\t0.8789\t0.7500\t0.2500\t0.8899\n",
            "         Recall: aspect, value\t0.6816\t0.3803\t0.0132\t0.3043\t0.4693\t0.0692\t0.7320\t0.7281\t0.8731\t0.6353\t0.0411\t0.5543\n",
            "       F1 score: aspect, value\t0.6347\t0.4779\t0.0260\t0.3944\t0.5638\t0.1268\t0.7613\t0.6945\t0.8760\t0.6879\t0.0706\t0.6831\n",
            "\n",
            "Over All ANSWER: aspect, value:----\t0.7491\t0.6135\t0.6745\n",
            "\n",
            "\n",
            "asp#1: RESTAURANT#GENERAL\n",
            "asp#2: DRINKS#QUALITY\n",
            "asp#3: DRINKS#PRICES\n",
            "asp#4: DRINKS#STYLE&OPTIONS\n",
            "asp#5: LOCATION#GENERAL\n",
            "asp#6: RESTAURANT#MISCELLANEOUS\n",
            "asp#7: FOOD#STYLE&OPTIONS\n",
            "asp#8: FOOD#PRICES\n",
            "asp#9: FOOD#QUALITY\n",
            "asp#10: AMBIENCE#GENERAL\n",
            "asp#11: RESTAURANT#PRICES\n",
            "asp#12: SERVICE#GENERAL\n"
          ]
        }
      ],
      "source": [
        "from processors.vlsp2018_processor import VLSP2018Parser\n",
        "VLSP2018Parser.vlsp_save_as(f'./predictions/{MODEL_NAME}.txt', raw_datasets['test']['Review'], y_pred, MULTI_BRANCH_NAMES)\n",
        "!java ../evaluators/SAEvaluate.java {TEST_PATH.replace('.csv', '.txt')} ./predictions/{MODEL_NAME}.txt"
      ]
    }
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