experiments/Restaurant-v1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmlWL-oDQ3Tw"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ds4v/absa-vlsp-2018/blob/main/experiments/Hotel-v1.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSyJrDH1WIJg",
        "outputId": "7cb95fda-a7e8-4cf7-da80-3a6086cdf330"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m431.4/431.4 kB\u001b[0m \u001b[31m9.0 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[31m33.2 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[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m26.8 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[31m20.7 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[31m1.0 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[31m63.3 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": "9cccb59a-df85-48be-9bd1-24e6e49d791d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'absa-vlsp-2018'...\n",
            "remote: Enumerating objects: 224, done.\u001b[K\n",
            "remote: Counting objects: 100% (135/135), done.\u001b[K\n",
            "remote: Compressing objects: 100% (95/95), done.\u001b[K\n",
            "remote: Total 224 (delta 61), reused 105 (delta 36), pack-reused 89\u001b[K\n",
            "Receiving objects: 100% (224/224), 31.67 MiB | 15.18 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": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ebq2fB17WIJh",
        "outputId": "9b1c5abf-9dad-4540-f19f-4a884f9f612e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.15.0'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import sys\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "sys.path.append('..')\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asXVePdEwpbc",
        "outputId": "258005ee-fb45-49ac-e9b4-2bb5c0981779"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using GPU: /device:GPU:0\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n"
          ]
        }
      ],
      "source": [
        "if tf.config.list_physical_devices('GPU'):\n",
        "    physical_devices = tf.config.list_physical_devices('GPU')\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
        "    print('Using GPU:', tf.test.gpu_device_name())\n",
        "    !nvcc -V\n",
        "else: raise ValueError('Running on CPU is not recommended.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Le1NSd1ZWIJi"
      },
      "source": [
        "# Constants Setup and Load the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "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-v1' # Multitask model on Restaurant domain\n",
        "MAX_LENGTH = 256\n",
        "BATCH_SIZE = 21\n",
        "EPOCHS = 20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtJDqdUUWIJk",
        "outputId": "9ef411cf-f0c6-434d-eb90-708a957cd070"
      },
      "outputs": [
        {
          "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",
              "})"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from processors.vlsp2018_processor import VLSP2018Loader\n",
        "raw_datasets = VLSP2018Loader.load(TRAIN_PATH, VAL_PATH, TEST_PATH)\n",
        "raw_datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2nSyWXOvWIJl"
      },
      "source": [
        "# Preprocess and Tokenize the Data"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
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        },
        "id": "43XpEFg9WIJl",
        "outputId": "cb80d7a7-1a3d-48d4-8c79-ede19fd798fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "VnCoreNLP word segmenter is loaded successfully.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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              "sentencepiece.bpe.model:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"
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        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "bmd1905/vietnamese-correction-v2 is loaded successfully.\n"
          ]
        }
      ],
      "source": [
        "from processors.vietnamese_processor import VietnameseTextPreprocessor\n",
        "\n",
        "# You should be carefull when using single word replacement for acronyms, because it can cause misinterpretation.\n",
        "# For example, 'giá': ['price', 'gia'] can replace the word 'gia' in 'gia đình', making it become 'giá đình'.\n",
        "vn_preprocessor = VietnameseTextPreprocessor(vncorenlp_dir='../processors/VnCoreNLP', extra_teencodes={\n",
        "    'khách sạn': ['ks'], 'nhà hàng': ['nhahang'], 'nhân viên': ['nv'],\n",
        "    'cửa hàng': ['store', 'sop', 'shopE', 'shop'],\n",
        "    'sản phẩm': ['sp', 'product'], 'hàng': ['hàg'],\n",
        "    'giao hàng': ['ship', 'delivery', 'síp'], 'đặt hàng': ['order'],\n",
        "    'chuẩn chính hãng': ['authentic', 'aut', 'auth'], 'hạn sử dụng': ['date', 'hsd'],\n",
        "    'điện thoại': ['dt'],  'facebook': ['fb', 'face'],\n",
        "    'nhắn tin': ['nt', 'ib'], 'trả lời': ['tl', 'trl', 'rep'],\n",
        "    'feedback': ['fback', 'fedback'], 'sử dụng': ['sd'], 'xài': ['sài'],\n",
        "}, max_correction_length=MAX_LENGTH)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgtRu6oMWIJm",
        "outputId": "82ac86b9-3d01-4e77-b3ef-07e268dd85c9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Encode: [0, 218, 8, 649, 212, 956, 2413, 195, 5, 2] \n",
            "Decode: <s> Tôi là sinh_viên trường đại_học Công_nghệ thông_tin. </s>\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "['input_ids', 'token_type_ids', 'attention_mask']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL)\n",
        "tokens = tokenizer.encode('Tôi là sinh_viên trường đại_học Công_nghệ thông_tin .')\n",
        "print('Encode:', tokens, '\\nDecode:', tokenizer.decode(tokens))\n",
        "tokenizer.model_input_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922,
          "referenced_widgets": [
            "129704d183794646a396e7ff39279fd6",
            "d1462848ac804660b573bf0cad835589",
            "3cf616855e57422590240982a95e57dd",
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            "d7c56046f6754700a518cbd50524c030",
            "eebe7f5b48934e96bde38023fb5c363a",
            "6c0e4703855146dab1d3cdceba4f5d94",
            "eadb4265465b4c28b25d8119fea8d062",
            "e6deb9db8113420a8ac20e83b4f40b1b",
            "ca9b56dfda2744a5b505c1c55624ffc6",
            "59617185aae04b8fbe70eb3bbb5e203b",
            "a79da9cf0631433b9298299a75058a99",
            "8924be2d71c94dce9ba1355ac02057e9",
            "f5baa5ce4b144e5cad53c8ad2dd29fdf",
            "3b34d550a2fd438f9705e57a36cd8e4b",
            "fbdf809ad3a34699bbd3330e40a2343f",
            "3c4bfc8c25484700ac550a057a872d55",
            "bda51655f2f948c88d7378c4b2409453",
            "3286ab7e6ccd442bb8500e7aba46fc7a",
            "fa151a65b2c241829269ce44871a3fc3",
            "eb167c1a7ae1461b9cbe0c7a5575c2d0",
            "40ca6b858ee345c2996cc51a7e5b0a5d",
            "000a5e4d1d174aabbeece48387b4bc5f"
          ]
        },
        "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": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8cd29af936154caca61cfc67d22a77cc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1290 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d7545f6110ea45b09d19e573422d286d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/500 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "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 . C\\u00f3 c\\u1ea3 b\\u00f2 ph\\u00f4 mai v\\u1edbi c\\u1ea3 b\\u1ea1ch tu\\u1ed9c n\\u01b0\\u1edbng n\\u01b0a- Th\\u1ecbt \\u1edf \\u0111\\u00e2y \\u01b0\\u1edbp r\\u1ea5t l\\u00e0 ngon v\\u1eeba mi\\u1ec7ng \\u0103n n\\u1eefa - N\\u00f3i ch\\u00f9n l\\u00e0 kh\\u00f4ng c\\u00f3 ch\\u1ed7 ch\\u00ea h\\u00ea h\\u00ea\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"encoded_input_ids\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"decoded_input_ids\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"<s> kimchi kimchi n\\u00f3i cho c\\u00f9ng th\\u00ec \\u0111\\u1ed3_\\u0103n c\\u00e0ng cay c\\u00e0ng ngon nh\\u01b0 l\\u1ea9u b\\u00e1nh g\\u1ea1o ph\\u1ea7n 4 ng\\u01b0\\u1eddi \\u0103n_\\u1edf kimchi kimchi 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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              "      <td>[0, 284, 690, 39, 2402, 57, 379, 15, 161, 6, 9...</td>\n",
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              "      <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",
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              "      <td>[0, 99, 509, 2418, 4600, 107, 802, 203, 4397, ...</td>\n",
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              "      <td>- Các bạn nhìn cái chảo này có to không 🙄🙄🙄- T...</td>\n",
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              "      <td>[0, 4951, 10, 36, 2263, 13, 9, 88, 437, 51, 68...</td>\n",
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              "      <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",
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              "    <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",
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              "      <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",
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              "  }\n",
              "\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
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              "      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": "markdown",
      "metadata": {
        "id": "y-Dk5nixWIJm"
      },
      "source": [
        "# Extra Step for ACSA-v1 Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSM6s-J4WIJn",
        "outputId": "15631617-ac66-46fa-d35a-5f7ab795a998"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[INFO] Transforming \"Aspect#Categoy,Polarity\" labels to flattened one-hot encoding...\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['FlattenOneHotLabels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 2961\n",
              "    })\n",
              "    val: Dataset({\n",
              "        features: ['FlattenOneHotLabels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 1290\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['FlattenOneHotLabels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 500\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_from_disk\n",
        "preprocessed_datasets = load_from_disk('../datasets/preprocessed_restaurant')\n",
        "preprocessed_datasets = VLSP2018Loader.labels_to_flatten_onehot(preprocessed_datasets)\n",
        "preprocessed_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pqwqSpMWIJn",
        "outputId": "00f1b8fe-e1e8-4b69-e8c7-28bb4f806c15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Aspect#Category,Polarity in One-hot form of the first review:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [0, 0, 0, 1],\n",
              "       [0, 0, 0, 1],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0],\n",
              "       [1, 0, 0, 0]])"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print('Aspect#Category,Polarity in One-hot form of the first review:')\n",
        "example_onehot = preprocessed_datasets['train'][0]['FlattenOneHotLabels']\n",
        "np.array(example_onehot).reshape(-1, 4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGRUGqTWWIJn"
      },
      "source": [
        "# Prepare for TensorFlow Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCWAdAozDcAE",
        "outputId": "01241cac-19b2-4161-fba7-067725e302b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['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"
          ]
        }
      ],
      "source": [
        "ASPECT_CATEGORY_NAMES = raw_datasets['train'].column_names[1:]\n",
        "steps_per_epoch = len(preprocessed_datasets['train']) // BATCH_SIZE\n",
        "total_steps = EPOCHS * steps_per_epoch\n",
        "print(ASPECT_CATEGORY_NAMES)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "AWlh8K7OWIJn"
      },
      "outputs": [],
      "source": [
        "train_tf_dataset = preprocessed_datasets['train'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols='FlattenOneHotLabels',\n",
        "    batch_size=BATCH_SIZE, shuffle=True, num_workers=8\n",
        ")\n",
        "val_tf_dataset = preprocessed_datasets['val'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols='FlattenOneHotLabels',\n",
        "    batch_size=BATCH_SIZE, shuffle=False, num_workers=8\n",
        ")\n",
        "test_tf_dataset = preprocessed_datasets['test'].to_tf_dataset(\n",
        "    columns=tokenizer.model_input_names, label_cols='FlattenOneHotLabels',\n",
        "    batch_size=BATCH_SIZE, shuffle=False, num_workers=8\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "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": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 948
        },
        "id": "Ots6QvfKNS9E",
        "outputId": "9eb42845-b0bf-49c4-ba06-f5997a75ec04"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at vinai/phobert-base were not used when initializing TFRobertaModel: ['lm_head']\n",
            "- This IS expected if you are initializing TFRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFRobertaModel were initialized from the model checkpoint at vinai/phobert-base.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model_2/roberta/pooler/dense/kernel:0', 'tf_roberta_model_2/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_2/roberta/pooler/dense/kernel:0', 'tf_roberta_model_2/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_2/roberta/pooler/dense/kernel:0', 'tf_roberta_model_2/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_2/roberta/pooler/dense/kernel:0', 'tf_roberta_model_2/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "141/141 [==============================] - 122s 456ms/step - loss: 0.2265 - val_loss: 0.1706\n",
            "Epoch 2/20\n",
            "141/141 [==============================] - 56s 357ms/step - loss: 0.1543 - val_loss: 0.1433\n",
            "Epoch 3/20\n",
            "141/141 [==============================] - 55s 350ms/step - loss: 0.1139 - val_loss: 0.1334\n",
            "Epoch 4/20\n",
            "141/141 [==============================] - 54s 346ms/step - loss: 0.0864 - val_loss: 0.1377\n",
            "Epoch 5/20\n",
            "141/141 [==============================] - 54s 347ms/step - loss: 0.0606 - val_loss: 0.1703\n",
            "Epoch 6/20\n",
            "141/141 [==============================] - ETA: 0s - loss: 0.0330Restoring model weights from the end of the best epoch: 3.\n",
            "141/141 [==============================] - 54s 348ms/step - loss: 0.0330 - val_loss: 0.1804\n",
            "Epoch 6: early stopping\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 4min 15s, sys: 34.3 s, total: 4min 49s\n",
            "Wall time: 6min 39s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "from acsa_model import VLSP2018MultiTask\n",
        "from helper import plot_training_history\n",
        "model = VLSP2018MultiTask(PRETRAINED_MODEL, ASPECT_CATEGORY_NAMES, optimizer, name=MODEL_NAME)\n",
        "\n",
        "history = model.fit(\n",
        "    train_tf_dataset,\n",
        "    validation_data = val_tf_dataset,\n",
        "    callbacks = [early_stop_callback],\n",
        "    epochs = EPOCHS,\n",
        "    verbose = 1\n",
        ").history\n",
        "\n",
        "model.save_weights(f'./weights/{MODEL_NAME}/{MODEL_NAME}', save_format='tf')\n",
        "plot_training_history(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bekGxelJmM6g",
        "outputId": "da727ca0-72c5-4d5a-df0f-aae384084c3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/GoogleDrive\n",
            "  adding: weights/Restaurant-v1/ (stored 0%)\n",
            "  adding: weights/Restaurant-v1/checkpoint (deflated 39%)\n",
            "  adding: weights/Restaurant-v1/Restaurant-v1.index (deflated 80%)\n",
            "  adding: weights/Restaurant-v1/Restaurant-v1.data-00000-of-00001 (deflated 32%)\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive # Move weights to Drive for faster downloading\n",
        "drive.mount('/content/GoogleDrive', force_remount=True)\n",
        "!zip -r ./weights/{MODEL_NAME}.zip ./weights/{MODEL_NAME}\n",
        "!mv ./weights/{MODEL_NAME}.zip /content/GoogleDrive/My\\ Drive"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELoAR9-Y9kSF"
      },
      "source": [
        "# Predict on the Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkqO1rs0Qrw0",
        "outputId": "54a0a421-476a-40bd-ffc2-28e5e0dd332e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some layers from the model checkpoint at vinai/phobert-base were not used when initializing TFRobertaModel: ['lm_head']\n",
            "- This IS expected if you are initializing TFRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFRobertaModel were initialized from the model checkpoint at vinai/phobert-base.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "62/62 [==============================] - 29s 104ms/step - loss: 0.1334\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.13335208594799042"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from acsa_model import VLSP2018MultiTask\n",
        "reloaded_model = VLSP2018MultiTask(PRETRAINED_MODEL, ASPECT_CATEGORY_NAMES, optimizer, name=MODEL_NAME)\n",
        "reloaded_model.load_weights(f'./weights/{MODEL_NAME}/{MODEL_NAME}') # Reload the Model\n",
        "reloaded_model.evaluate(val_tf_dataset, batch_size=BATCH_SIZE, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuW9rBsnWIJo",
        "outputId": "427a7dc8-0d2e-4066-da35-71117c28222a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "24/24 [==============================] - 16s 142ms/step\n",
            "24/24 [==============================] - 8s 104ms/step - loss: 0.2770\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#PRICES,positive\n",
            "=> DRINKS#QUALITY,positive\n",
            "=> DRINKS#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": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2C-dHffWIJp",
        "outputId": "a37f0946-6e5f-4222-987d-3457b989b09f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your review: Giá cũng ok mà nhân viên hơi thiếu chuyên nghiệp\n",
            "[INFO] Preprocessing and tokenizing text data...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 7s 7s/step\n",
            "=> FOOD#PRICES,positive\n",
            "=> FOOD#QUALITY,neutral\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": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFTFgzTNWIJp",
        "outputId": "a29cbf3b-8da2-47db-8c65-efb9b7063959"
      },
      "outputs": [
        {
          "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]])"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from helper import argmax_label_matrix\n",
        "y_test = argmax_label_matrix(preprocessed_datasets['test']['FlattenOneHotLabels'])\n",
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 848
        },
        "id": "u_6eV6CGWIJp",
        "outputId": "68a9706d-fbc6-4c47-8394-22824844b0c2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 15/15 [00:00<00:00, 43751.43it/s]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "            <div style=\"display: flex; align-items: flex-start; flex-wrap: nowrap\">\n",
              "                <table style=\"margin-right: 10px\">\n",
              "            <tr>\n",
              "                <th style=\"font-weight: bold; text-align: center;\" rowspan=\"2\">ACSA Report (w/o \"None\" polarity)</th>\n",
              "                <th style=\"font-weight: bold; text-align: center;\" colspan=\"4\">Aspect#Category</th>\n",
              "                <th style=\"font-weight: bold; text-align: center;\" colspan=\"4\">Aspect#Category,Polarity</th>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>precision</th><th>recall</th><th>f1-score</th><th>support</th><th>precision</th><th>recall</th><th>f1-score</th><th>support</th>\n",
              "            </tr>\n",
              "        \n",
              "                    <tr>\n",
              "                        <td>AMBIENCE#GENERAL,negative</td>\n",
              "                        <td rowspan=\"3\">0.866</td><td rowspan=\"3\">0.835</td><td rowspan=\"3\">0.850</td><td rowspan=\"3\">255</td>\n",
              "                        <td>0.333</td><td>0.077</td><td>0.125</td><td>26</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>AMBIENCE#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.748</td><td>0.867</td><td>0.803</td><td>181</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>AMBIENCE#GENERAL,neutral</td>\n",
              "                        \n",
              "                        <td>0.367</td><td>0.229</td><td>0.282</td><td>48</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#PRICES,negative</td>\n",
              "                        <td rowspan=\"3\">0.800</td><td rowspan=\"3\">0.158</td><td rowspan=\"3\">0.264</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>0.071</td><td>0.036</td><td>0.048</td><td>28</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#PRICES,neutral</td>\n",
              "                        \n",
              "                        <td>0.000</td><td>0.000</td><td>0.000</td><td>45</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>DRINKS#QUALITY,negative</td>\n",
              "                        <td rowspan=\"3\">0.878</td><td rowspan=\"3\">0.606</td><td rowspan=\"3\">0.717</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>0.694</td><td>0.630</td><td>0.660</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\">0.656</td><td rowspan=\"3\">0.457</td><td rowspan=\"3\">0.538</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>0.562</td><td>0.439</td><td>0.493</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\">0.806</td><td rowspan=\"3\">0.943</td><td rowspan=\"3\">0.869</td><td rowspan=\"3\">331</td>\n",
              "                        <td>0.667</td><td>0.286</td><td>0.400</td><td>28</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#PRICES,positive</td>\n",
              "                        \n",
              "                        <td>0.462</td><td>0.844</td><td>0.597</td><td>128</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#PRICES,neutral</td>\n",
              "                        \n",
              "                        <td>0.716</td><td>0.577</td><td>0.639</td><td>175</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#QUALITY,negative</td>\n",
              "                        <td rowspan=\"3\">0.977</td><td rowspan=\"3\">0.939</td><td rowspan=\"3\">0.958</td><td rowspan=\"3\">457</td>\n",
              "                        <td>0.500</td><td>0.091</td><td>0.154</td><td>11</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#QUALITY,positive</td>\n",
              "                        \n",
              "                        <td>0.919</td><td>0.923</td><td>0.921</td><td>403</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#QUALITY,neutral</td>\n",
              "                        \n",
              "                        <td>0.594</td><td>0.442</td><td>0.507</td><td>43</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#STYLE&OPTIONS,negative</td>\n",
              "                        <td rowspan=\"3\">0.929</td><td rowspan=\"3\">0.782</td><td rowspan=\"3\">0.849</td><td rowspan=\"3\">403</td>\n",
              "                        <td>0.333</td><td>0.125</td><td>0.182</td><td>16</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#STYLE&OPTIONS,positive</td>\n",
              "                        \n",
              "                        <td>0.825</td><td>0.790</td><td>0.807</td><td>334</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>FOOD#STYLE&OPTIONS,neutral</td>\n",
              "                        \n",
              "                        <td>0.308</td><td>0.075</td><td>0.121</td><td>53</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,negative</td>\n",
              "                        <td rowspan=\"3\">0.920</td><td rowspan=\"3\">0.642</td><td rowspan=\"3\">0.757</td><td rowspan=\"3\">179</td>\n",
              "                        <td>0.375</td><td>0.188</td><td>0.250</td><td>16</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.600</td><td>0.281</td><td>0.383</td><td>64</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>LOCATION#GENERAL,neutral</td>\n",
              "                        \n",
              "                        <td>0.667</td><td>0.586</td><td>0.624</td><td>99</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#GENERAL,negative</td>\n",
              "                        <td rowspan=\"3\">0.841</td><td rowspan=\"3\">0.166</td><td rowspan=\"3\">0.277</td><td rowspan=\"3\">223</td>\n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>13</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.841</td><td>0.180</td><td>0.297</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\">1.000</td><td rowspan=\"3\">0.115</td><td rowspan=\"3\">0.207</td><td rowspan=\"3\">130</td>\n",
              "                        <td>1.000</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>0.600</td><td>0.102</td><td>0.174</td><td>59</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#MISCELLANEOUS,neutral</td>\n",
              "                        \n",
              "                        <td>0.600</td><td>0.048</td><td>0.090</td><td>62</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#PRICES,negative</td>\n",
              "                        <td rowspan=\"3\">0.000</td><td rowspan=\"3\">0.000</td><td rowspan=\"3\">0.000</td><td rowspan=\"3\">73</td>\n",
              "                        <td>1.000</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>0.000</td><td>0.000</td><td>0.000</td><td>44</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>RESTAURANT#PRICES,neutral</td>\n",
              "                        \n",
              "                        <td>1.000</td><td>0.000</td><td>0.000</td><td>24</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,negative</td>\n",
              "                        <td rowspan=\"3\">0.703</td><td rowspan=\"3\">0.840</td><td rowspan=\"3\">0.766</td><td rowspan=\"3\">175</td>\n",
              "                        <td>0.600</td><td>0.360</td><td>0.450</td><td>25</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,positive</td>\n",
              "                        \n",
              "                        <td>0.807</td><td>0.750</td><td>0.777</td><td>128</td>\n",
              "                    </tr>\n",
              "                \n",
              "                    <tr>\n",
              "                        <td>SERVICE#GENERAL,neutral</td>\n",
              "                        \n",
              "                        <td>0.213</td><td>0.727</td><td>0.330</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>0.814544</td>\n",
              "      <td>0.932142</td>\n",
              "      <td>0.869384</td>\n",
              "      <td>3581.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>0.756297</td>\n",
              "      <td>0.665668</td>\n",
              "      <td>0.708094</td>\n",
              "      <td>1669.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>0.510204</td>\n",
              "      <td>0.157233</td>\n",
              "      <td>0.240385</td>\n",
              "      <td>159.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>neutral</th>\n",
              "      <td>0.552083</td>\n",
              "      <td>0.358714</td>\n",
              "      <td>0.434872</td>\n",
              "      <td>591.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.781000</td>\n",
              "      <td>0.781000</td>\n",
              "      <td>0.781000</td>\n",
              "      <td>0.781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.658282</td>\n",
              "      <td>0.528439</td>\n",
              "      <td>0.563184</td>\n",
              "      <td>6000.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.764424</td>\n",
              "      <td>0.781000</td>\n",
              "      <td>0.765051</td>\n",
              "      <td>6000.000</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>0.781</td>\n",
              "      <td>0.682</td>\n",
              "      <td>0.423</td>\n",
              "      <td>0.416</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aspect#Category</th>\n",
              "      <td>0.833</td>\n",
              "      <td>0.784</td>\n",
              "      <td>0.570</td>\n",
              "      <td>0.609</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Polarity</th>\n",
              "      <td>0.781</td>\n",
              "      <td>0.658</td>\n",
              "      <td>0.528</td>\n",
              "      <td>0.563</td>\n",
              "      <td>6000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "                </div>\n",
              "            </div>\n",
              "        "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from evaluators.sklearn_evaluator import VLSP2018SklearnEvaluator\n",
        "sk_eval = VLSP2018SklearnEvaluator(y_test, y_pred, ASPECT_CATEGORY_NAMES)\n",
        "# sk_eval.report(report_type='Aspect#Category,Polarity').round(3)\n",
        "# sk_eval.report(report_type='Aspect#Category').round(3)\n",
        "# sk_eval.report(report_type='Polarity').round(3)\n",
        "# sk_eval.report(report_type='macro_avg').round(3)\n",
        "sk_eval.report(report_type='all')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLhItgRyWIJp"
      },
      "source": [
        "# VLSP Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yt4As64AoFYz",
        "outputId": "b0ed6243-6726-47ce-9304-7db2a9b15eb1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "500it [00:00, 76723.20it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row mismatch:﻿#1 <-> #1\n",
            "Evaluation Result >> File:./predictions/Restaurant-v1.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\t44\t49\t15\t32\t125\t15\t339\t387\t439\t246\t2\t209\n",
            "\n",
            "        Correct ANSWER: aspect\t37\t42\t11\t20\t115\t15\t315\t312\t429\t213\t0\t147\n",
            "             Precision: aspect\t0.8409\t0.8571\t0.7333\t0.6250\t0.9200\t1.0000\t0.9292\t0.8062\t0.9772\t0.8659\t0.0000\t0.7033\n",
            "                Recall: aspect\t0.1659\t0.5915\t0.1447\t0.4348\t0.6425\t0.1154\t0.7816\t0.9426\t0.9387\t0.8353\t0.0000\t0.8400\n",
            "              F1 score: aspect\t0.2772\t0.7000\t0.2418\t0.5128\t0.7566\t0.2069\t0.8491\t0.8691\t0.9576\t0.8503\t0.0000\t0.7656\n",
            "\n",
            "  Over All ANSWER: aspect:----\t0.8707\t0.6846\t0.7665\n",
            "\n",
            "  Correct ANSWER: aspect,value\t37\t33\t1\t18\t79\t9\t270\t217\t392\t170\t0\t121\n",
            "      Precision: aspect, value\t0.8409\t0.6735\t0.0667\t0.5625\t0.6320\t0.6000\t0.7965\t0.5607\t0.8929\t0.6911\t0.0000\t0.5789\n",
            "         Recall: aspect, value\t0.1659\t0.4648\t0.0132\t0.3913\t0.4413\t0.0692\t0.6700\t0.6556\t0.8578\t0.6667\t0.0000\t0.6914\n",
            "       F1 score: aspect, value\t0.2772\t0.5500\t0.0220\t0.4615\t0.5197\t0.1241\t0.7278\t0.6045\t0.8750\t0.6786\t0.0000\t0.6302\n",
            "\n",
            "Over All ANSWER: aspect, value:----\t0.7082\t0.5568\t0.6235\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, ASPECT_CATEGORY_NAMES)\n",
        "!java ../evaluators/SAEvaluate.java {TEST_PATH.replace('.csv', '.txt')} ./predictions/{MODEL_NAME}.txt"
      ]
    }
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