model/FineTuning_Emot_6_labels.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4NkBPpJBwNe3",
   "metadata": {
    "id": "4NkBPpJBwNe3"
   },
   "source": [
    "# Import Datasets & Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "459abb20",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "459abb20",
    "outputId": "3467b32f-e8e1-4d01-afb8-4cc35b263cf2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cloning into 'Emotion-Predictor-App'...\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/azizp128/Emotion-Predictor-App.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "tlLbiig4FZx_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tlLbiig4FZx_",
    "outputId": "a63a612d-8b97-428a-e7f7-8ae375c04815"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: transformers in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (4.46.3)\n",
      "Requirement already satisfied: filelock in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (3.13.1)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (0.26.3)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (2.0.1)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (24.2)\n",
      "Requirement already satisfied: pyyaml>=5.1 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (6.0.2)\n",
      "Requirement already satisfied: regex!=2019.12.17 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (2024.11.6)\n",
      "Requirement already satisfied: requests in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (2.32.3)\n",
      "Requirement already satisfied: tokenizers<0.21,>=0.20 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (0.20.3)\n",
      "Requirement already satisfied: safetensors>=0.4.1 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (0.4.5)\n",
      "Requirement already satisfied: tqdm>=4.27 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from transformers) (4.67.1)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.10.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.11.0)\n",
      "Requirement already satisfied: colorama in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from tqdm>=4.27->transformers) (0.4.6)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from requests->transformers) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from requests->transformers) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from requests->transformers) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\tools\\anaconda3\\envs\\transformers\\lib\\site-packages (from requests->transformers) (2024.8.30)\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e071f6c3",
   "metadata": {
    "id": "e071f6c3"
   },
   "outputs": [],
   "source": [
    "# COLLAB\n",
    "import os, sys\n",
    "\n",
    "sys.path.append('../')\n",
    "os.chdir('../')\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "from tqdm import tqdm\n",
    "\n",
    "from transformers import BertForSequenceClassification, BertConfig, BertTokenizer\n",
    "from emotion_predictor_app.utils.forward_fn import forward_sequence_classification\n",
    "from emotion_predictor_app.utils.metrics import document_sentiment_metrics_fn\n",
    "from emotion_predictor_app.utils.data_utils import EmotionDetectionDataset, EmotionDetectionDataLoader\n",
    "\n",
    "# LOCAL \n",
    "# from pathlib import Path\n",
    "# sys.path.append(str(Path.cwd().parent))  # Move one level up\n",
    "# from utils.forward_fn import forward_sequence_classification\n",
    "# from utils.metrics import document_sentiment_metrics_fn\n",
    "# from utils.data_utils import EmotionDetectionDataset, EmotionDetectionDataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb833053",
   "metadata": {
    "id": "eb833053"
   },
   "outputs": [],
   "source": [
    "###\n",
    "# common functions\n",
    "###\n",
    "def set_seed(seed):\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    \n",
    "def count_param(module, trainable=False):\n",
    "    if trainable:\n",
    "        return sum(p.numel() for p in module.parameters() if p.requires_grad)\n",
    "    else:\n",
    "        return sum(p.numel() for p in module.parameters())\n",
    "    \n",
    "def get_lr(optimizer):\n",
    "    for param_group in optimizer.param_groups:\n",
    "        return param_group['lr']\n",
    "\n",
    "def metrics_to_string(metric_dict):\n",
    "    string_list = []\n",
    "    for key, value in metric_dict.items():\n",
    "        string_list.append('{}:{:.2f}'.format(key, value))\n",
    "    return ' '.join(string_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a641083",
   "metadata": {
    "id": "2a641083"
   },
   "outputs": [],
   "source": [
    "# Set random seed\n",
    "set_seed(26092020)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f2c9a2",
   "metadata": {
    "id": "78f2c9a2"
   },
   "source": [
    "# Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c468a8c0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234,
     "referenced_widgets": [
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    "id": "c468a8c0",
    "outputId": "46d6571e-dfa1-40ed-f0f0-bccb274ef3dc"
   },
   "outputs": [],
   "source": [
    "# Load Tokenizer and Config\n",
    "tokenizer = BertTokenizer.from_pretrained('indobenchmark/indobert-base-p1')\n",
    "config = BertConfig.from_pretrained('indobenchmark/indobert-base-p1')\n",
    "config.num_labels = EmotionDetectionDataset.NUM_LABELS\n",
    "\n",
    "# Instantiate model\n",
    "model = BertForSequenceClassification.from_pretrained('indobenchmark/indobert-base-p1', config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "558d66eb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "558d66eb",
    "outputId": "e99f8117-ecd7-422c-ace3-b016a9a607d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(50000, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=6, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36a9f37a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "36a9f37a",
    "outputId": "650b7613-2cca-4ecb-d061-f4e9136bd5c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124445958"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_param(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c8f790",
   "metadata": {
    "id": "63c8f790"
   },
   "source": [
    "# Prepare Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a18183ca",
   "metadata": {
    "id": "a18183ca"
   },
   "outputs": [],
   "source": [
    "train_dataset_path = '...' # fill with your dataset path\n",
    "valid_dataset_path = '...'\n",
    "test_dataset_path = '...'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HinKVyHUiF82",
   "metadata": {
    "id": "HinKVyHUiF82"
   },
   "source": [
    "## Check dataset info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "BXj_1DrWh3_Q",
   "metadata": {
    "id": "BXj_1DrWh3_Q"
   },
   "outputs": [],
   "source": [
    "train_dataset = pd.read_csv(train_dataset_path)\n",
    "valid_dataset = pd.read_csv(valid_dataset_path)\n",
    "test_dataset = pd.read_csv(test_dataset_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "VPHSRvbTiDOG",
   "metadata": {
    "colab": {
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     "height": 206
    },
    "id": "VPHSRvbTiDOG",
    "outputId": "5f19d4a3-f09b-4781-f2f9-1edab0b649af"
   },
   "outputs": [
    {
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       "     label                                              tweet\n",
       "0     fear   mestinya hrs diselepetan spt itu karena sdh g...\n",
       "1    happy    Antara rahsia untuk senang berjaya dan tenan...\n",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21000 entries, 0 to 20999\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   label   21000 non-null  object\n",
      " 1   tweet   21000 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 328.2+ KB\n"
     ]
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   label                                              tweet\n",
       "0  anger  Kalo marah marah pelariannya ke twitter kalo s...\n",
       "1   fear    Halo hooman Aku mama masih mencari keluarga ...\n",
       "2   love  Cinta itu lebih mementingkan perasaan pasangan...\n",
       "3  happy    Bismillah hari pertama kerja salah satu peru...\n",
       "4   love  It s Okay That s Love Cinta bukan tentang memb..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "A2a1EtmpkLTn",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A2a1EtmpkLTn",
    "outputId": "9ac17e88-6dd8-4bd4-95d1-e2ffd78bac67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9000 entries, 0 to 8999\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   label   9000 non-null   object\n",
      " 1   tweet   9000 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 140.8+ KB\n"
     ]
    }
   ],
   "source": [
    "valid_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1pH8_QshiFeC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "1pH8_QshiFeC",
    "outputId": "d048c6a2-e4ba-4189-e1b6-ac84b7e35e7e"
   },
   "outputs": [
    {
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      "text/plain": [
       "   label                                              tweet\n",
       "0  happy   mestinya hrs diselepetan spt itu karena sdh g...\n",
       "1  happy    Antara rahsia untuk senang berjaya dan tenan...\n",
       "2  happy   Aku pamit duluan ya Denger kata ini tuh kadan...\n",
       "3  happy   askrl Udah jauh banget ngobrol sama kating di...\n",
       "4  happy                         Senang mendengar nya kawan"
      ]
     },
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     "metadata": {},
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    }
   ],
   "source": [
    "test_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "uBQr2zpZkM_w",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "uBQr2zpZkM_w",
    "outputId": "58c8e98c-371d-44b1-a19d-7b6ee25c37af"
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      "<class 'pandas.core.frame.DataFrame'>\n",
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      " 0   label   5000 non-null   object\n",
      " 1   tweet   5000 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 78.2+ KB\n"
     ]
    }
   ],
   "source": [
    "test_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "XKjAIiVniKTJ",
   "metadata": {
    "id": "XKjAIiVniKTJ"
   },
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08ee0c2d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "08ee0c2d",
    "outputId": "df5f2ac8-6d09-4624-f261-6a77ba331e51",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n"
     ]
    }
   ],
   "source": [
    "train_dataset = EmotionDetectionDataset(train_dataset_path, tokenizer, lowercase=True)\n",
    "valid_dataset = EmotionDetectionDataset(valid_dataset_path, tokenizer, lowercase=True)\n",
    "test_dataset = EmotionDetectionDataset(test_dataset_path, tokenizer, lowercase=True)\n",
    "\n",
    "train_loader = EmotionDetectionDataLoader(dataset=train_dataset, max_seq_len=512, batch_size=8, num_workers=16, shuffle=True)  \n",
    "valid_loader = EmotionDetectionDataLoader(dataset=valid_dataset, max_seq_len=512, batch_size=8, num_workers=16, shuffle=False)  \n",
    "test_loader = EmotionDetectionDataLoader(dataset=test_dataset, max_seq_len=512, batch_size=8, num_workers=16, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e31ccda9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e31ccda9",
    "outputId": "adb62865-1fc4-4f39-8754-6ed11bc5fddc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sadness': 0, 'anger': 1, 'love': 2, 'fear': 3, 'happy': 4, 'disgust': 5}\n",
      "{0: 'sadness', 1: 'anger', 2: 'love', 3: 'fear', 4: 'happy', 5: 'disgust'}\n"
     ]
    }
   ],
   "source": [
    "w2i, i2w = EmotionDetectionDataset.LABEL2INDEX, EmotionDetectionDataset.INDEX2LABEL\n",
    "print(w2i)\n",
    "print(i2w)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466452d6",
   "metadata": {
    "id": "466452d6"
   },
   "source": [
    "# Test model on sample sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11d9a4bd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "11d9a4bd",
    "outputId": "8e1612c6-e4a6-4574-c9ec-8ea166d26162"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Bahagia hatiku melihat pernikahan putri sulungku yang cantik jelita | Label : disgust (32.396%)\n"
     ]
    }
   ],
   "source": [
    "text = 'Bahagia hatiku melihat pernikahan putri sulungku yang cantik jelita'\n",
    "subwords = tokenizer.encode(text)\n",
    "subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)\n",
    "\n",
    "logits = model(subwords)[0]\n",
    "label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()\n",
    "\n",
    "print(f'Text: {text} | Label : {i2w[label]} ({F.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffc8b2a4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ffc8b2a4",
    "outputId": "0053ec31-3cd0-4394-b545-89d7c8467770"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Budi pergi ke pondok indah mall membeli cakwe | Label : disgust (31.867%)\n"
     ]
    }
   ],
   "source": [
    "text = 'Budi pergi ke pondok indah mall membeli cakwe'\n",
    "subwords = tokenizer.encode(text)\n",
    "subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)\n",
    "\n",
    "logits = model(subwords)[0]\n",
    "label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()\n",
    "\n",
    "print(f'Text: {text} | Label : {i2w[label]} ({F.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e79bd1e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1e79bd1e",
    "outputId": "8bec6b10-6487-4efa-c960-c4dc8c519f76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Dasar anak sialan!! Kurang ajar!! | Label : disgust (28.371%)\n"
     ]
    }
   ],
   "source": [
    "text = 'Dasar anak sialan!! Kurang ajar!!'\n",
    "subwords = tokenizer.encode(text)\n",
    "subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)\n",
    "\n",
    "logits = model(subwords)[0]\n",
    "label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()\n",
    "\n",
    "print(f'Text: {text} | Label : {i2w[label]} ({F.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d828e4",
   "metadata": {
    "id": "42d828e4"
   },
   "source": [
    "# Fine Tuning & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ee2d69f",
   "metadata": {
    "id": "4ee2d69f"
   },
   "outputs": [],
   "source": [
    "optimizer = optim.Adam(model.parameters(), lr=5e-6)\n",
    "model = model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4f22a95",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b4f22a95",
    "outputId": "a0049cb4-d941-4cfb-b918-df590d156df6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(Epoch 1) TRAIN LOSS:0.1798 LR:0.00000500: 100%|██████████| 2625/2625 [04:45<00:00,  9.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 1) TRAIN LOSS:0.1798 ACC:0.95 F1:0.95 REC:0.95 PRE:0.95 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0784 ACC:0.97 F1:0.97 REC:0.97 PRE:0.97: 100%|██████████| 1125/1125 [01:38<00:00, 11.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 1) VALID LOSS:0.0784 ACC:0.97 F1:0.97 REC:0.97 PRE:0.97\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 2) TRAIN LOSS:0.0495 LR:0.00000500: 100%|██████████| 2625/2625 [04:41<00:00,  9.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 2) TRAIN LOSS:0.0495 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0678 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:37<00:00, 11.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 2) VALID LOSS:0.0678 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 3) TRAIN LOSS:0.0295 LR:0.00000500: 100%|██████████| 2625/2625 [04:41<00:00,  9.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 3) TRAIN LOSS:0.0295 ACC:0.99 F1:0.99 REC:0.99 PRE:0.99 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0685 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:36<00:00, 11.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 3) VALID LOSS:0.0685 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 4) TRAIN LOSS:0.0218 LR:0.00000500: 100%|██████████| 2625/2625 [04:42<00:00,  9.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 4) TRAIN LOSS:0.0218 ACC:0.99 F1:0.99 REC:0.99 PRE:0.99 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0822 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:34<00:00, 11.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 4) VALID LOSS:0.0822 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 5) TRAIN LOSS:0.0169 LR:0.00000500: 100%|██████████| 2625/2625 [04:43<00:00,  9.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 5) TRAIN LOSS:0.0169 ACC:0.99 F1:0.99 REC:0.99 PRE:0.99 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0751 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:35<00:00, 11.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 5) VALID LOSS:0.0751 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 6) TRAIN LOSS:0.0135 LR:0.00000500: 100%|██████████| 2625/2625 [04:42<00:00,  9.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 6) TRAIN LOSS:0.0135 ACC:0.99 F1:0.99 REC:0.99 PRE:0.99 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0817 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:34<00:00, 11.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 6) VALID LOSS:0.0817 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 7) TRAIN LOSS:0.0108 LR:0.00000500: 100%|██████████| 2625/2625 [04:42<00:00,  9.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 7) TRAIN LOSS:0.0108 ACC:1.00 F1:1.00 REC:1.00 PRE:1.00 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0908 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:37<00:00, 11.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 7) VALID LOSS:0.0908 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 8) TRAIN LOSS:0.0104 LR:0.00000500: 100%|██████████| 2625/2625 [04:42<00:00,  9.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 8) TRAIN LOSS:0.0104 ACC:1.00 F1:1.00 REC:1.00 PRE:1.00 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0986 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:37<00:00, 11.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 8) VALID LOSS:0.0986 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 9) TRAIN LOSS:0.0097 LR:0.00000500: 100%|██████████| 2625/2625 [04:41<00:00,  9.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 9) TRAIN LOSS:0.0097 ACC:1.00 F1:1.00 REC:1.00 PRE:1.00 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0945 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:36<00:00, 11.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 9) VALID LOSS:0.0945 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/2625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "(Epoch 10) TRAIN LOSS:0.0088 LR:0.00000500: 100%|██████████| 2625/2625 [04:42<00:00,  9.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 10) TRAIN LOSS:0.0088 ACC:1.00 F1:1.00 REC:1.00 PRE:1.00 LR:0.00000500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/1125 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "VALID LOSS:0.0962 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98: 100%|██████████| 1125/1125 [01:36<00:00, 11.63it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Epoch 10) VALID LOSS:0.0962 ACC:0.98 F1:0.98 REC:0.98 PRE:0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Train\n",
    "n_epochs = 10\n",
    "for epoch in range(n_epochs):\n",
    "    model.train()\n",
    "    torch.set_grad_enabled(True)\n",
    " \n",
    "    total_train_loss = 0\n",
    "    list_hyp, list_label = [], []\n",
    "\n",
    "    train_pbar = tqdm(train_loader, leave=True, total=len(train_loader))\n",
    "    for i, batch_data in enumerate(train_pbar):\n",
    "        # Forward model\n",
    "        loss, batch_hyp, batch_label = forward_sequence_classification(model, batch_data[:-1], i2w=i2w, device='cuda')\n",
    "\n",
    "        # Update model\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        tr_loss = loss.item()\n",
    "        total_train_loss = total_train_loss + tr_loss\n",
    "\n",
    "        # Calculate metrics\n",
    "        list_hyp += batch_hyp\n",
    "        list_label += batch_label\n",
    "\n",
    "        train_pbar.set_description(\"(Epoch {}) TRAIN LOSS:{:.4f} LR:{:.8f}\".format((epoch+1),\n",
    "            total_train_loss/(i+1), get_lr(optimizer)))\n",
    "\n",
    "    # Calculate train metric\n",
    "    metrics = document_sentiment_metrics_fn(list_hyp, list_label)\n",
    "    print(\"(Epoch {}) TRAIN LOSS:{:.4f} {} LR:{:.8f}\".format((epoch+1),\n",
    "        total_train_loss/(i+1), metrics_to_string(metrics), get_lr(optimizer)))\n",
    "\n",
    "    # Evaluate on validation\n",
    "    model.eval()\n",
    "    torch.set_grad_enabled(False)\n",
    "    \n",
    "    total_loss, total_correct, total_labels = 0, 0, 0\n",
    "    list_hyp, list_label = [], []\n",
    "\n",
    "    pbar = tqdm(valid_loader, leave=True, total=len(valid_loader))\n",
    "    for i, batch_data in enumerate(pbar):\n",
    "        batch_seq = batch_data[-1]        \n",
    "        loss, batch_hyp, batch_label = forward_sequence_classification(model, batch_data[:-1], i2w=i2w, device='cuda')\n",
    "        \n",
    "        # Calculate total loss\n",
    "        valid_loss = loss.item()\n",
    "        total_loss = total_loss + valid_loss\n",
    "\n",
    "        # Calculate evaluation metrics\n",
    "        list_hyp += batch_hyp\n",
    "        list_label += batch_label\n",
    "        metrics = document_sentiment_metrics_fn(list_hyp, list_label)\n",
    "\n",
    "        pbar.set_description(\"VALID LOSS:{:.4f} {}\".format(total_loss/(i+1), metrics_to_string(metrics)))\n",
    "        \n",
    "    metrics = document_sentiment_metrics_fn(list_hyp, list_label)\n",
    "    print(\"(Epoch {}) VALID LOSS:{:.4f} {}\".format((epoch+1),\n",
    "        total_loss/(i+1), metrics_to_string(metrics)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9026103",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c9026103",
    "outputId": "1ecc1926-67d3-4432-b2a9-4bac214c54d3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r  0%|          | 0/625 [00:00<?, ?it/s]/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n",
      "100%|██████████| 625/625 [00:15<00:00, 40.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      index    label\n",
      "0         0     fear\n",
      "1         1    happy\n",
      "2         2  sadness\n",
      "3         3     fear\n",
      "4         4    happy\n",
      "...     ...      ...\n",
      "4995   4995  disgust\n",
      "4996   4996  disgust\n",
      "4997   4997  disgust\n",
      "4998   4998  sadness\n",
      "4999   4999    anger\n",
      "\n",
      "[5000 rows x 2 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate on test\n",
    "model.eval()\n",
    "torch.set_grad_enabled(False)\n",
    "\n",
    "total_loss, total_correct, total_labels = 0, 0, 0\n",
    "list_hyp, list_label = [], []\n",
    "\n",
    "pbar = tqdm(test_loader, leave=True, total=len(test_loader))\n",
    "for i, batch_data in enumerate(pbar):\n",
    "    _, batch_hyp, _ = forward_sequence_classification(model, batch_data[:-1], i2w=i2w, device='cuda')\n",
    "    list_hyp += batch_hyp\n",
    "\n",
    "# Save prediction\n",
    "df = pd.DataFrame({'label':list_hyp}).reset_index()\n",
    "df.to_csv('pred.txt', index=False)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5ff2961",
   "metadata": {
    "id": "f5ff2961"
   },
   "source": [
    "# Test fine-tuned model on sample sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd57c818",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fd57c818",
    "outputId": "ccf4f2a1-055f-4fdf-a1fa-bda9cd8a9663"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Bahagia hatiku melihat pernikahan putri sulungku yang cantik jelita | Label : love (99.960%)\n"
     ]
    }
   ],
   "source": [
    "text = 'Bahagia hatiku melihat pernikahan putri sulungku yang cantik jelita'\n",
    "subwords = tokenizer.encode(text)\n",
    "subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)\n",
    "\n",
    "logits = model(subwords)[0]\n",
    "label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()\n",
    "\n",
    "print(f'Text: {text} | Label : {i2w[label]} ({F.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12a317ed",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "12a317ed",
    "outputId": "34df7d4e-35b3-4981-99df-233a11f3bebe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Jorok sekali kamu, makan tidak cuci tangan dahulu | Label : disgust (99.931%)\n"
     ]
    }
   ],
   "source": [
    "text = 'Jorok sekali kamu, makan tidak cuci tangan dahulu'\n",
    "subwords = tokenizer.encode(text)\n",
    "subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)\n",
    "\n",
    "logits = model(subwords)[0]\n",
    "label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()\n",
    "\n",
    "print(f'Text: {text} | Label : {i2w[label]} ({F.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77a21baf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "77a21baf",
    "outputId": "6b74ba9f-576b-46e2-b4dc-1fe6314cf657"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Dasar anak sialan!! Kurang ajar!! | Label : anger (98.820%)\n"
     ]
    }
   ],
   "source": [
    "text = 'Dasar anak sialan!! Kurang ajar!!'\n",
    "subwords = tokenizer.encode(text)\n",
    "subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)\n",
    "\n",
    "logits = model(subwords)[0]\n",
    "label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()\n",
    "\n",
    "print(f'Text: {text} | Label : {i2w[label]} ({F.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aa50c15",
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   "source": [
    "# Coba kalimat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a590f499",
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    },
    "id": "a590f499",
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masukan kalimat :saya habis jatuh dari tangga\n",
      "Text: saya habis jatuh dari tangga | Label : sadness (99.912%)\n"
     ]
    }
   ],
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    "User_input = input('Masukan kalimat :')\n",
    "text = User_input\n",
    "subwords = tokenizer.encode(text)\n",
    "subwords = torch.LongTensor(subwords).view(1, -1).to(model.device)\n",
    "\n",
    "logits = model(subwords)[0]\n",
    "label = torch.topk(logits, k=1, dim=-1)[1].squeeze().item()\n",
    "\n",
    "print(f'Text: {text} | Label : {i2w[label]} ({F.softmax(logits, dim=-1).squeeze()[label] * 100:.3f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92be0ab",
   "metadata": {},
   "source": [
    "# Import Pre-trained model using pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90ffef7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a pipeline as a high-level helper\n",
    "from transformers import pipeline\n",
    "\n",
    "device = 0 if torch.cuda.is_available() else -1\n",
    "\n",
    "pipe = pipeline(\"sentiment-analysis\", model=\"azizp128/prediksi-emosi-indobert\", device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28f0551a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment: JIJIK\n",
      "Score: 0.9954845905303955\n"
     ]
    }
   ],
   "source": [
    "result = pipe(\"menjijikkan sekali!\")\n",
    "print(f\"Sentiment: {result[0]['label']}\\nScore: {result[0]['score']}\")"
   ]
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