diff --git a/21_2DM_YouTubeDislike_1.ipynb b/21_2DM_YouTubeDislike_1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ba820d1b7fbe52c291c182071e92bc2f67cc6ba5
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+++ b/21_2DM_YouTubeDislike_1.ipynb
@@ -0,0 +1,2051 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "21-2DM_TeamProject_YouTubeDislike.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/gdrive')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vQJ0kj57vTnG",
+ "outputId": "e2d2444e-431c-4a3c-f791-a8d73278462a"
+ },
+ "execution_count": 47,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# 1. Data Collection & 2. Data Preprocessing(Cleaning and Integration)"
+ ],
+ "metadata": {
+ "id": "gmdvXi4AvUnz"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {
+ "id": "_rX1nfJcWnW6"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas\n",
+ "import re\n",
+ "import random\n",
+ "from googleapiclient.discovery import build\n",
+ "\n",
+ "\n",
+ "def cleansing(text):\n",
+ " pattern = '[0-9]+:[0-9]+' # comment timestamp\n",
+ " text = re.sub(pattern=pattern, repl='', string=text)\n",
+ " pattern = '([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+)' # E-mail제거\n",
+ " text = re.sub(pattern=pattern, repl='', string=text)\n",
+ " pattern = '(http|ftp|https)://(?:[-\\w.]|(?:%[\\da-fA-F]{2}))+' # URL제거\n",
+ " text = re.sub(pattern=pattern, repl='', string=text)\n",
+ " pattern = '([ㄱ-ㅎㅏ-ㅣ]+)' # 한글 자음, 모음 제거\n",
+ " text = re.sub(pattern=pattern, repl='', string=text)\n",
+ " pattern = '<[^>]*>' # HTML 태그 제거\n",
+ " text = re.sub(pattern=pattern, repl='', string=text)\n",
+ " pattern = '[^\\w\\s]' # 특수기호제거\n",
+ " text = re.sub(pattern=pattern, repl='', string=text)\n",
+ " return text\n",
+ "\n",
+ "def collection(videoId, outputName):\n",
+ " # key\n",
+ " _developerKey = \"\"\n",
+ " _videoId = videoId # 테스트 할 영상 ID\n",
+ "\n",
+ " # get video information\n",
+ " comments = list()\n",
+ " api_obj = build('youtube', 'v3', developerKey=_developerKey)\n",
+ " video_info = api_obj.videos().list(part='statistics, snippet', id=_videoId).execute()\n",
+ "\n",
+ " print(\"\\n==========\")\n",
+ " print(\"title: \", video_info['items'][0]['snippet']['title'])\n",
+ " print(\"view: \", video_info['items'][0]['statistics']['viewCount'])\n",
+ " print(\"like: \", video_info['items'][0]['statistics']['likeCount'], \" / dislike: \", video_info['items'][0]['statistics']['dislikeCount'])\n",
+ "\n",
+ " response = api_obj.commentThreads().list(part='snippet,replies', videoId=_videoId, maxResults=100, textFormat='plainText').execute()\n",
+ "\n",
+ " while response:\n",
+ " for item in response['items']:\n",
+ " comment = item['snippet']['topLevelComment']['snippet']\n",
+ " text = cleansing(comment['textDisplay']).strip()\n",
+ " if text and (not text.isspace()):\n",
+ " comments.append(text)\n",
+ "\n",
+ " if len(comments) >= 10000:\n",
+ " break\n",
+ "\n",
+ " if 'nextPageToken' in response:\n",
+ " response = api_obj.commentThreads().list(part='snippet,replies', videoId=_videoId,\n",
+ " pageToken=response['nextPageToken'], maxResults=100).execute()\n",
+ " else:\n",
+ " break\n",
+ "\n",
+ " if len(comments) > 1000:\n",
+ " comments = random.sample(comments, 1000)\n",
+ "\n",
+ " print(len(comments))\n",
+ " print(\"실제 좋싫비: \", int(video_info['items'][0]['statistics']['likeCount']) / (int(video_info['items'][0]['statistics']['likeCount']) + int(video_info['items'][0]['statistics']['dislikeCount'])))\n",
+ "\n",
+ "\n",
+ " df = pandas.DataFrame(comments)\n",
+ " df.to_excel(outputName+'_data.xlsx', header=False, index=None)\n",
+ " return video_info['items'][0]['statistics']['likeCount']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "count_like = collection(\"gSIQM6cipNs\", \"results\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "i1OF_UGMVFCW",
+ "outputId": "ff59c819-5889-4fd1-9b40-ad22738b3a8c"
+ },
+ "execution_count": 49,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "title: 펄어비스 도깨비 - 첫 공개 게임플레이\n",
+ "view: 1649019\n",
+ "like: 19846 / dislike: 444\n",
+ "1000\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# 2. Data Preprocessing (Feature Extraction)"
+ ],
+ "metadata": {
+ "id": "ryfjao5Jvv81"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i45d7E0L8bZ_"
+ },
+ "source": [
+ "### 2-1. 준비 사항"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WkAHQrj2Vjbl",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "18092b9a-69f1-4908-8706-e81e2daea5d8"
+ },
+ "source": [
+ "# Hugging Face의 트랜스포머 모델을 설치\n",
+ "!pip install transformers"
+ ],
+ "execution_count": 50,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.12.5)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
+ "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.46)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
+ "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n",
+ "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.2.1)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
+ "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n",
+ "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
+ "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n",
+ "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
+ "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
+ "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
+ "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
+ "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "75dIz2fNWG8F"
+ },
+ "source": [
+ "import tensorflow as tf\n",
+ "import torch\n",
+ "\n",
+ "from transformers import BertTokenizer\n",
+ "from transformers import BertForSequenceClassification, AdamW, BertConfig\n",
+ "from transformers import get_linear_schedule_with_warmup\n",
+ "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
+ "from keras.preprocessing.sequence import pad_sequences\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import random\n",
+ "import time\n",
+ "import datetime"
+ ],
+ "execution_count": 51,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "h_U3uMySBCIV"
+ },
+ "source": [
+ "### 2-2. 데이터 로드"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ImBtAkSyTW1r",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c093b573-85a8-4e5c-d7cb-811f1837e318"
+ },
+ "source": [
+ "# 네이버 영화리뷰 감정분석 데이터 다운로드\n",
+ "!git clone https://github.com/bab2min/corpus.git\n",
+ "\n",
+ "# 디렉토리의 파일 목록\n",
+ "!ls corpus/sentiment -la"
+ ],
+ "execution_count": 52,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "fatal: destination path 'corpus' already exists and is not an empty directory.\n",
+ "total 31520\n",
+ "drwxr-xr-x 2 root root 4096 Dec 9 05:17 .\n",
+ "drwxr-xr-x 4 root root 4096 Dec 9 05:17 ..\n",
+ "-rw-r--r-- 1 root root 20623547 Dec 9 05:17 naver_shopping.txt\n",
+ "-rw-r--r-- 1 root root 4617 Dec 9 05:17 README.md\n",
+ "-rw-r--r-- 1 root root 11631607 Dec 9 05:17 steam.txt\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0LPEdb2tWfIU",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "ad8dd131-74a8-46ae-dd07-d41f3cdb5ce0"
+ },
+ "source": [
+ "# 데이터 로드\n",
+ "total_data = pd.read_table('corpus/sentiment/steam.txt',names=['label','reviews'])\n",
+ "print('전체 리뷰 개수 : ',len(total_data))"
+ ],
+ "execution_count": 53,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "전체 리뷰 개수 : 100000\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tejY9ZhABYWl"
+ },
+ "source": [
+ "# # 훈련셋의 앞부분 출력\n",
+ "# total_data.head(10)\n",
+ "\n",
+ "# # total_data['label'] = np.select([total_data.ratings > 3], [1], default=0)\n",
+ "# total_data[:5]"
+ ],
+ "execution_count": 54,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "C6SsoAb-ZLpZ",
+ "outputId": "6ef80c02-f484-4009-bb6c-c7928966c787"
+ },
+ "source": [
+ "# 총 데이터\n",
+ "total_data['reviews'].nunique(), total_data['label'].nunique()\n",
+ "total_data.drop_duplicates(subset=['reviews'], inplace=True) # reviews 열에서 중복인 내용이 있다면 중복 제거\n",
+ "print('총 샘플의 수 :',len(total_data))"
+ ],
+ "execution_count": 55,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "총 샘플의 수 : 99892\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# 훈련 데이터와 테스트 데이터를 3:1 비율로 분리\n",
+ "train_data, test_data = train_test_split(total_data, test_size = 0.25, random_state = 42)\n",
+ "print('훈련용 리뷰의 개수 :', len(train_data))\n",
+ "print('테스트용 리뷰의 개수 :', len(test_data))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "PC0eCj-nfnDZ",
+ "outputId": "c2fe0db4-c4a9-4515-995f-aec8bfb931b7"
+ },
+ "execution_count": 56,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "훈련용 리뷰의 개수 : 74919\n",
+ "테스트용 리뷰의 개수 : 24973\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "XgjMzosCDD35"
+ },
+ "source": [
+ "### 2-3. 전처리 - 훈련셋"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2GoESQ0jbybJ"
+ },
+ "source": [
+ "# 리뷰 문장 추출\n",
+ "sentences = train_data['reviews']\n",
+ "#sentences[:10]"
+ ],
+ "execution_count": 57,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# BERT의 입력 형식에 맞게 변환\n",
+ "sentences = [\"[CLS] \" + str(sentence) + \" [SEP]\" for sentence in sentences]\n",
+ "#sentences[:10]"
+ ],
+ "metadata": {
+ "id": "tRLpY5OgfsiG"
+ },
+ "execution_count": 58,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ww1Or7bUoj2K"
+ },
+ "source": [
+ "# import matplotlib.pyplot as plt\n",
+ "\n",
+ "# fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5))\n",
+ "# text_len = train_data[train_data['label']==1]['tokenized'].map(lambda x: len(x))\n",
+ "# ax1.hist(text_len, color='red')\n",
+ "# ax1.set_title('Positive Reviews')\n",
+ "# ax1.set_xlabel('length of samples')\n",
+ "# ax1.set_ylabel('number of samples')\n",
+ "# print('긍정 리뷰의 평균 길이 :', np.mean(text_len))\n",
+ "\n",
+ "# text_len = train_data[train_data['label']==0]['tokenized'].map(lambda x: len(x))\n",
+ "# ax2.hist(text_len, color='blue')\n",
+ "# ax2.set_title('Negative Reviews')\n",
+ "# fig.suptitle('Words in texts')\n",
+ "# ax2.set_xlabel('length of samples')\n",
+ "# ax2.set_ylabel('number of samples')\n",
+ "# print('부정 리뷰의 평균 길이 :', np.mean(text_len))\n",
+ "# plt.show()"
+ ],
+ "execution_count": 59,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7hBblIVQcXJR",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "58029075-b9f5-4e2a-aab3-f7353f82e00e"
+ },
+ "source": [
+ "# 라벨 추출\n",
+ "labels = train_data['label'].values\n",
+ "labels"
+ ],
+ "execution_count": 60,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1, 0, 0, ..., 1, 0, 1])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 60
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PwEplfDvcnZG"
+ },
+ "source": [
+ "# BERT의 토크나이저로 문장을 토큰으로 분리\n",
+ "tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased', do_lower_case=False)\n",
+ "tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]\n",
+ "\n",
+ "# print (sentences[0])\n",
+ "# print (tokenized_texts[0])"
+ ],
+ "execution_count": 61,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VJ76KiP_dLn-",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "1443b1a1-fe69-41e6-db60-a64ed69e7b9a"
+ },
+ "source": [
+ "# 입력 토큰의 최대 시퀀스 길이\n",
+ "MAX_LEN = 80\n",
+ "\n",
+ "# 토큰을 숫자 인덱스로 변환\n",
+ "input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]\n",
+ "\n",
+ "# 문장을 MAX_LEN 길이에 맞게 자르고, 모자란 부분을 패딩 0으로 채움\n",
+ "input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\n",
+ "\n",
+ "input_ids[0]"
+ ],
+ "execution_count": 62,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([ 101, 66402, 9836, 12692, 12965, 9955, 9137, 18382,\n",
+ " 77039, 9415, 118884, 11664, 9709, 12692, 12508, 9523,\n",
+ " 119081, 48345, 9670, 89523, 9654, 73306, 9913, 119228,\n",
+ " 66402, 58303, 48345, 9765, 38631, 33188, 48345, 102,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 62
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "pKfL8SotdVaW",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "9725bd47-8bc6-4a1d-92a1-8311e51c3d94"
+ },
+ "source": [
+ "# 어텐션 마스크 초기화\n",
+ "attention_masks = []\n",
+ "\n",
+ "# 어텐션 마스크를 패딩이 아니면 1, 패딩이면 0으로 설정\n",
+ "# 패딩 부분은 BERT 모델에서 어텐션을 수행하지 않아 속도 향상\n",
+ "for seq in input_ids:\n",
+ " seq_mask = [float(i>0) for i in seq]\n",
+ " attention_masks.append(seq_mask)\n",
+ "\n",
+ "print(attention_masks[0])"
+ ],
+ "execution_count": 63,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1f5Vq3-7eNKH",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "b8909609-3915-4bb2-d3cb-5cd9cb44bca8"
+ },
+ "source": [
+ "# 훈련셋과 검증셋으로 분리\n",
+ "train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(input_ids,\n",
+ " labels, \n",
+ " random_state=2018, \n",
+ " test_size=0.1)\n",
+ "\n",
+ "# 어텐션 마스크를 훈련셋과 검증셋으로 분리\n",
+ "train_masks, validation_masks, _, _ = train_test_split(attention_masks, \n",
+ " input_ids,\n",
+ " random_state=2018, \n",
+ " test_size=0.1)\n",
+ "\n",
+ "# 데이터를 파이토치의 텐서로 변환\n",
+ "train_inputs = torch.tensor(train_inputs)\n",
+ "train_labels = torch.tensor(train_labels)\n",
+ "train_masks = torch.tensor(train_masks)\n",
+ "validation_inputs = torch.tensor(validation_inputs)\n",
+ "validation_labels = torch.tensor(validation_labels)\n",
+ "validation_masks = torch.tensor(validation_masks)\t\t\t\t\n",
+ "\n",
+ "print(train_inputs[0])\n",
+ "print(train_labels[0])\n",
+ "print(train_masks[0])\n",
+ "print(validation_inputs[0])\n",
+ "print(validation_labels[0])\n",
+ "print(validation_masks[0])"
+ ],
+ "execution_count": 64,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "tensor([ 101, 9955, 118627, 12092, 9555, 11664, 8982, 19105, 8924,\n",
+ " 56710, 12508, 9283, 31401, 118632, 28578, 100, 9546, 118837,\n",
+ " 14153, 9960, 21711, 14843, 12508, 39420, 31401, 118632, 11903,\n",
+ " 9407, 118624, 10003, 14863, 12453, 9954, 35465, 12092, 100,\n",
+ " 9405, 12508, 23811, 24982, 48549, 102, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0])\n",
+ "tensor(0)\n",
+ "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0.])\n",
+ "tensor([ 101, 9954, 118663, 119383, 18622, 119212, 9960, 16323, 24982,\n",
+ " 48549, 119, 102, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0])\n",
+ "tensor(1)\n",
+ "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0.])\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I3vlyUJuVRo5"
+ },
+ "source": [
+ "# 배치 사이즈\n",
+ "batch_size = 60\n",
+ "\n",
+ "# 파이토치의 DataLoader로 입력, 마스크, 라벨을 묶어 데이터 설정\n",
+ "# 학습시 배치 사이즈 만큼 데이터를 가져옴\n",
+ "\n",
+ "train_data = TensorDataset(train_inputs, train_masks, train_labels)\n",
+ "train_sampler = RandomSampler(train_data)\n",
+ "train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
+ "\n",
+ "validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)\n",
+ "validation_sampler = SequentialSampler(validation_data)\n",
+ "validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)"
+ ],
+ "execution_count": 65,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zkqUHx51dffp"
+ },
+ "source": [
+ "### 2-4. 전처리 - 테스트셋"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xgrsNuArd4pj"
+ },
+ "source": [
+ "# 리뷰 문장 추출\n",
+ "sentences = test_data['reviews']\n",
+ "#sentences[:10]"
+ ],
+ "execution_count": 66,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Gtz3QZt9d4pz"
+ },
+ "source": [
+ "# BERT의 입력 형식에 맞게 변환\n",
+ "sentences = [\"[CLS] \" + str(sentence) + \" [SEP]\" for sentence in sentences]\n",
+ "#sentences[:10]"
+ ],
+ "execution_count": 67,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "li8oRajbd4p3",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "a2afeb8f-a9ef-4c9c-8e9a-1e45fd6ef44d"
+ },
+ "source": [
+ "# 라벨 추출\n",
+ "labels = test_data['label'].values\n",
+ "labels"
+ ],
+ "execution_count": 68,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([0, 0, 1, ..., 1, 1, 0])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 68
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lvpQ49nEd4p6"
+ },
+ "source": [
+ "# BERT의 토크나이저로 문장을 토큰으로 분리\n",
+ "tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased', do_lower_case=False)\n",
+ "tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]\n",
+ "\n",
+ "#print (sentences[0])\n",
+ "#print (tokenized_texts[0])"
+ ],
+ "execution_count": 69,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HI9viuAvd4p_",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "dd0772f6-17f3-43fc-c4ef-6d0941727c02"
+ },
+ "source": [
+ "# 입력 토큰의 최대 시퀀스 길이\n",
+ "MAX_LEN = 80\n",
+ "\n",
+ "# 토큰을 숫자 인덱스로 변환\n",
+ "input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]\n",
+ "\n",
+ "# 문장을 MAX_LEN 길이에 맞게 자르고, 모자란 부분을 패딩 0으로 채움\n",
+ "input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\n",
+ "\n",
+ "input_ids[0]"
+ ],
+ "execution_count": 70,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([ 101, 9640, 48446, 66402, 10892, 9405, 11664, 8982,\n",
+ " 14867, 10003, 14863, 33188, 48345, 119, 9995, 118992,\n",
+ " 108436, 23969, 28911, 9029, 119266, 77884, 100, 102,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 70
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "v1NKmP0Fd4qD",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "11c0e1f3-75b0-43a1-e14f-562828d91dc1"
+ },
+ "source": [
+ "# 어텐션 마스크 초기화\n",
+ "attention_masks = []\n",
+ "\n",
+ "# 어텐션 마스크를 패딩이 아니면 1, 패딩이면 0으로 설정\n",
+ "# 패딩 부분은 BERT 모델에서 어텐션을 수행하지 않아 속도 향상\n",
+ "for seq in input_ids:\n",
+ " seq_mask = [float(i>0) for i in seq]\n",
+ " attention_masks.append(seq_mask)\n",
+ "\n",
+ "print(attention_masks[0])"
+ ],
+ "execution_count": 71,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RIkaYCGbd4qG",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c7c0e706-2f80-47f6-9e61-162a56f5c212"
+ },
+ "source": [
+ "# 데이터를 파이토치의 텐서로 변환\n",
+ "test_inputs = torch.tensor(input_ids)\n",
+ "test_labels = torch.tensor(labels)\n",
+ "test_masks = torch.tensor(attention_masks)\n",
+ "\n",
+ "\n",
+ "print(test_inputs[0])\n",
+ "print(test_labels[0])\n",
+ "print(test_masks[0])"
+ ],
+ "execution_count": 72,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "tensor([ 101, 9640, 48446, 66402, 10892, 9405, 11664, 8982, 14867,\n",
+ " 10003, 14863, 33188, 48345, 119, 9995, 118992, 108436, 23969,\n",
+ " 28911, 9029, 119266, 77884, 100, 102, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0])\n",
+ "tensor(0)\n",
+ "tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+ " 0., 0., 0., 0., 0., 0., 0., 0.])\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7gwdYv1Ad4qK"
+ },
+ "source": [
+ "# 배치 사이즈\n",
+ "batch_size = 60\n",
+ "\n",
+ "# 파이토치의 DataLoader로 입력, 마스크, 라벨을 묶어 데이터 설정\n",
+ "# 학습시 배치 사이즈 만큼 데이터를 가져옴\n",
+ "test_data = TensorDataset(test_inputs, test_masks, test_labels)\n",
+ "test_sampler = RandomSampler(test_data)\n",
+ "test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)"
+ ],
+ "execution_count": 73,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FBvpU-Hfgcth"
+ },
+ "source": [
+ "### 2-5. 모델 생성"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "heToD1ev0mOg",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c9c01380-2b5b-4df4-9efb-231aa4c3e4b0"
+ },
+ "source": [
+ "# GPU 디바이스 이름 구함\n",
+ "device_name = tf.test.gpu_device_name()\n",
+ "\n",
+ "# GPU 디바이스 이름 검사\n",
+ "if device_name == '/device:GPU:0':\n",
+ " print('Found GPU at: {}'.format(device_name))\n",
+ "else:\n",
+ " raise SystemError('GPU device not found')"
+ ],
+ "execution_count": 74,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Found GPU at: /device:GPU:0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "f6enIxvt1FB2",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "8a420062-53a0-4000-d4fd-b4efdfe99351"
+ },
+ "source": [
+ "# 디바이스 설정\n",
+ "if torch.cuda.is_available(): \n",
+ " device = torch.device(\"cuda\")\n",
+ " print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
+ " print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
+ "else:\n",
+ " device = torch.device(\"cpu\")\n",
+ " print('No GPU available, using the CPU instead.')"
+ ],
+ "execution_count": 75,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "There are 1 GPU(s) available.\n",
+ "We will use the GPU: Tesla K80\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MS2MXSiLg5zC",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "ccf3fb70-9760-4df8-e732-d6452553eea8"
+ },
+ "source": [
+ "# 분류를 위한 BERT 모델 생성\n",
+ "model = BertForSequenceClassification.from_pretrained(\"bert-base-multilingual-cased\", num_labels=2)\n",
+ "model.cuda()"
+ ],
+ "execution_count": 76,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias']\n",
+ "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "BertForSequenceClassification(\n",
+ " (bert): BertModel(\n",
+ " (embeddings): BertEmbeddings(\n",
+ " (word_embeddings): Embedding(119547, 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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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",
+ " )\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=2, bias=True)\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 76
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### 2-6. Prediction"
+ ],
+ "metadata": {
+ "id": "ILwVMwib29g5"
+ }
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZIdfbLTuWmxk"
+ },
+ "source": [
+ "# 옵티마이저 설정\n",
+ "optimizer = AdamW(model.parameters(),\n",
+ " lr = 2e-5, # 학습률\n",
+ " eps = 1e-8 # 0으로 나누는 것을 방지하기 위한 epsilon 값\n",
+ " )\n",
+ "\n",
+ "# 에폭수\n",
+ "epochs = 1\n",
+ "\n",
+ "# 총 훈련 스텝 : 배치반복 횟수 * 에폭\n",
+ "total_steps = len(train_dataloader) * epochs\n",
+ "\n",
+ "# 처음에 학습률을 조금씩 변화시키는 스케줄러 생성\n",
+ "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
+ " num_warmup_steps = 0,\n",
+ " num_training_steps = total_steps)"
+ ],
+ "execution_count": 77,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gzCHV_ghj7DM"
+ },
+ "source": [
+ "### 2-7. 모델 학습"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "S0-p6pPVXCRe"
+ },
+ "source": [
+ "# 정확도 계산 함수\n",
+ "def flat_accuracy(preds, labels):\n",
+ " \n",
+ " pred_flat = np.argmax(preds, axis=1).flatten()\n",
+ " labels_flat = labels.flatten()\n",
+ "\n",
+ " return np.sum(pred_flat == labels_flat) / len(labels_flat)\n",
+ "\n",
+ "def precision(preds, labels):\n",
+ " # 실제 맞은거 / 내가 맞다고 한거\n",
+ " pred_flat = np.argmax(preds, axis=1).flatten() # 예측한 결과물 리스트\n",
+ " labels_flat = labels.flatten() # 정답 리스트\n",
+ " a=np.sum(pred_flat==True) + np.sum(labels_flat==True) - np.sum(pred_flat!=labels_flat)\n",
+ " b=np.sum(pred_flat==True) + np.sum(labels_flat==False) - np.sum(pred_flat==labels_flat)\n",
+ " return a/(a+b)\n",
+ "\n",
+ "def recall(preds,lables):\n",
+ " # 실제 맞은거/ 실제 맞은거 + 아니라고 했는데 맞은거 \n",
+ " pred_flat = np.argmax(preds, axis=1).flatten()\n",
+ " labels_flat = labels.flatten()\n",
+ " a= np.sum(pred_flat==True) + np.sum(labels_flat==True) - np.sum(pred_flat!=labels_flat)\n",
+ " b= np.sum(pred_flat==False) + np.sum(labels_flat ==True) - np.sum(pred_flat==labels_flat)\n",
+ " return a/(a+b)\n",
+ "\n",
+ "\n"
+ ],
+ "execution_count": 78,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FJXISnJzCdLM"
+ },
+ "source": [
+ "# 시간 표시 함수\n",
+ "def format_time(elapsed):\n",
+ "\n",
+ " # 반올림\n",
+ " elapsed_rounded = int(round((elapsed)))\n",
+ " \n",
+ " # hh:mm:ss으로 형태 변경\n",
+ " return str(datetime.timedelta(seconds=elapsed_rounded))"
+ ],
+ "execution_count": 79,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "muU2kS2GCh4y",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "132d4da4-0ebe-431d-c7a6-4b66915234a6"
+ },
+ "source": [
+ "# 재현을 위해 랜덤시드 고정\n",
+ "seed_val = 42\n",
+ "random.seed(seed_val)\n",
+ "np.random.seed(seed_val)\n",
+ "torch.manual_seed(seed_val)\n",
+ "torch.cuda.manual_seed_all(seed_val)\n",
+ "\n",
+ "# 그래디언트 초기화\n",
+ "model.zero_grad()\n",
+ "\n",
+ "# 에폭만큼 반복\n",
+ "for epoch_i in range(0, epochs):\n",
+ " \n",
+ " # ========================================\n",
+ " # Training\n",
+ " # ========================================\n",
+ " \n",
+ " print(\"\")\n",
+ " print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
+ " print('Training...')\n",
+ "\n",
+ " # 시작 시간 설정\n",
+ " t0 = time.time()\n",
+ "\n",
+ " # 로스 초기화\n",
+ " total_loss = 0\n",
+ "\n",
+ " # 훈련모드로 변경\n",
+ " model.train()\n",
+ " \n",
+ " # 데이터로더에서 배치만큼 반복하여 가져옴\n",
+ " for step, batch in enumerate(train_dataloader):\n",
+ " # 경과 정보 표시\n",
+ " if step % 500 == 0 and not step == 0:\n",
+ " elapsed = format_time(time.time() - t0)\n",
+ " print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n",
+ "\n",
+ " # 배치를 GPU에 넣음\n",
+ " batch = tuple(t.to(device) for t in batch)\n",
+ " \n",
+ " # 배치에서 데이터 추출\n",
+ " b_input_ids, b_input_mask, b_labels = batch\n",
+ "\n",
+ " # Forward 수행 \n",
+ " outputs = model(b_input_ids, \n",
+ " token_type_ids=None, \n",
+ " attention_mask=b_input_mask, \n",
+ " labels=b_labels)\n",
+ " \n",
+ " # 로스 구함\n",
+ " loss = outputs[0]\n",
+ "\n",
+ " # 총 로스 계산\n",
+ " total_loss += loss.item()\n",
+ "\n",
+ " # Backward 수행으로 그래디언트 계산\n",
+ " loss.backward()\n",
+ "\n",
+ " # 그래디언트 클리핑\n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
+ "\n",
+ " # 그래디언트를 통해 가중치 파라미터 업데이트\n",
+ " optimizer.step()\n",
+ "\n",
+ " # 스케줄러로 학습률 감소\n",
+ " scheduler.step()\n",
+ "\n",
+ " # 그래디언트 초기화\n",
+ " model.zero_grad()\n",
+ "\n",
+ " # 평균 로스 계산\n",
+ " avg_train_loss = total_loss / len(train_dataloader) \n",
+ "\n",
+ " print(\"\")\n",
+ " print(\" Average training loss: {0:.2f}\".format(avg_train_loss))\n",
+ " \n",
+ " \n",
+ " print(\" Training epcoh took: {:}\".format(format_time(time.time() - t0)))\n",
+ " \n",
+ " # ========================================\n",
+ " # Validation\n",
+ " # ========================================\n",
+ "\n",
+ " print(\"\")\n",
+ " print(\"Running Validation...\")\n",
+ "\n",
+ " #시작 시간 설정\n",
+ " t0 = time.time()\n",
+ "\n",
+ " # 평가모드로 변경\n",
+ " model.eval()\n",
+ "\n",
+ " # 변수 초기화\n",
+ " eval_loss, eval_accuracy = 0, 0\n",
+ " eval_precision, eval_recall = 0, 0\n",
+ " nb_eval_steps, nb_eval_examples = 0, 0\n",
+ "\n",
+ " # 데이터로더에서 배치만큼 반복하여 가져옴\n",
+ " for batch in validation_dataloader:\n",
+ " # 배치를 GPU에 넣음\n",
+ " batch = tuple(t.to(device) for t in batch)\n",
+ " \n",
+ " # 배치에서 데이터 추출\n",
+ " b_input_ids, b_input_mask, b_labels = batch\n",
+ " \n",
+ " # 그래디언트 계산 안함\n",
+ " with torch.no_grad(): \n",
+ " # Forward 수행\n",
+ " outputs = model(b_input_ids, \n",
+ " token_type_ids=None, \n",
+ " attention_mask=b_input_mask)\n",
+ " \n",
+ " # 로스 구함\n",
+ " logits = outputs[0]\n",
+ "\n",
+ " # CPU로 데이터 이동\n",
+ " logits = logits.detach().cpu().numpy()\n",
+ " label_ids = b_labels.to('cpu').numpy()\n",
+ " \n",
+ " # 출력 로짓과 라벨을 비교하여 정확도 계산\n",
+ " tmp_eval_accuracy = flat_accuracy(logits, label_ids)\n",
+ " eval_accuracy += tmp_eval_accuracy\n",
+ " temp_eval_precision = precision(logits,label_ids)\n",
+ " eval_precision += temp_eval_precision\n",
+ " temp_eval_recall = recall(logits,label_ids)\n",
+ " eval_recall += temp_eval_recall\n",
+ " nb_eval_steps += 1\n",
+ "\n",
+ " print(\" Accuracy: {0:.5f}\".format(eval_accuracy/nb_eval_steps))\n",
+ " print(\" Precision: {0:.5f}\".format(eval_precision/nb_eval_steps))\n",
+ " print(\" Recall: {0:.5f}\".format(eval_recall/nb_eval_steps))\n",
+ " print(\" Validation took: {:}\".format(format_time(time.time() - t0)))\n",
+ "\n",
+ "print(\"\")\n",
+ "print(\"Training complete!\")"
+ ],
+ "execution_count": 80,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "======== Epoch 1 / 1 ========\n",
+ "Training...\n",
+ " Batch 500 of 1,124. Elapsed: 0:13:09.\n",
+ " Batch 1,000 of 1,124. Elapsed: 0:26:18.\n",
+ "\n",
+ " Average training loss: 0.52\n",
+ " Training epcoh took: 0:29:34\n",
+ "\n",
+ "Running Validation...\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:21: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Accuracy: 0.77688\n",
+ " Precision: 0.78540\n",
+ " Recall: 0.49996\n",
+ " Validation took: 0:01:13\n",
+ "\n",
+ "Training complete!\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hxlXEBA0WefL"
+ },
+ "source": [
+ "에폭마다 훈련셋과 검증셋을 반복하여 학습을 수행합니다. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6BVbl4Zjatzn"
+ },
+ "source": [
+ "### 2-8. 테스트셋 평가"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "c5KHb6RkbHdj",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "ffe3784b-c180-4e1a-c964-6a9bd66a45e5"
+ },
+ "source": [
+ "#시작 시간 설정\n",
+ "t0 = time.time()\n",
+ "\n",
+ "# 평가모드로 변경\n",
+ "model.eval()\n",
+ "\n",
+ "# 변수 초기화\n",
+ "eval_loss, eval_accuracy = 0, 0\n",
+ "eval_precision, eval_recall = 0, 0\n",
+ "nb_eval_steps, nb_eval_examples = 0, 0\n",
+ "\n",
+ "# 데이터로더에서 배치만큼 반복하여 가져옴\n",
+ "for step, batch in enumerate(test_dataloader):\n",
+ " # 경과 정보 표시\n",
+ " if step % 100 == 0 and not step == 0:\n",
+ " elapsed = format_time(time.time() - t0)\n",
+ " print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(test_dataloader), elapsed))\n",
+ "\n",
+ " # 배치를 GPU에 넣음\n",
+ " batch = tuple(t.to(device) for t in batch)\n",
+ " \n",
+ " # 배치에서 데이터 추출\n",
+ " b_input_ids, b_input_mask, b_labels = batch\n",
+ " \n",
+ " # 그래디언트 계산 안함\n",
+ " with torch.no_grad(): \n",
+ " # Forward 수행\n",
+ " outputs = model(b_input_ids, \n",
+ " token_type_ids=None, \n",
+ " attention_mask=b_input_mask)\n",
+ " \n",
+ " # 로스 구함\n",
+ " logits = outputs[0]\n",
+ "\n",
+ " # CPU로 데이터 이동\n",
+ " logits = logits.detach().cpu().numpy()\n",
+ " label_ids = b_labels.to('cpu').numpy()\n",
+ " \n",
+ " # 출력 로짓과 라벨을 비교하여 정확도 계산\n",
+ " tmp_eval_accuracy = flat_accuracy(logits, label_ids)\n",
+ " eval_accuracy += tmp_eval_accuracy\n",
+ " temp_eval_precision = precision(logits,label_ids)\n",
+ " eval_precision += temp_eval_precision\n",
+ " temp_eval_recall = recall(logits,label_ids)\n",
+ " eval_recall += temp_eval_recall\n",
+ " nb_eval_steps += 1\n",
+ "\n",
+ "print(\"\")\n",
+ "print(\"Accuracy: {0:.5f}\".format(eval_accuracy/nb_eval_steps))\n",
+ "print(\" Precision: {0:.5f}\".format(eval_precision/nb_eval_steps))\n",
+ "print(\" Recall: {0:.5f}\".format(eval_recall/nb_eval_steps))\n",
+ "\n",
+ "print(\"Test took: {:}\".format(format_time(time.time() - t0)))"
+ ],
+ "execution_count": 81,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:21: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Batch 100 of 417. Elapsed: 0:00:59.\n",
+ " Batch 200 of 417. Elapsed: 0:01:57.\n",
+ " Batch 300 of 417. Elapsed: 0:02:56.\n",
+ " Batch 400 of 417. Elapsed: 0:03:55.\n",
+ "\n",
+ "Accuracy: 0.77245\n",
+ " Precision: 0.77396\n",
+ " Recall: 0.49996\n",
+ "Test took: 0:04:04\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U7SzL1IBe1Dm"
+ },
+ "source": [
+ "### **2-9. 새로운 문장 테스트**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Tb4v_VfEfGQB"
+ },
+ "source": [
+ "# 입력 데이터 변환\n",
+ "def convert_input_data(sentences):\n",
+ "\n",
+ " # BERT의 토크나이저로 문장을 토큰으로 분리\n",
+ " tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]\n",
+ "\n",
+ " # 입력 토큰의 최대 시퀀스 길이\n",
+ " MAX_LEN = 128\n",
+ "\n",
+ " # 토큰을 숫자 인덱스로 변환\n",
+ " input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]\n",
+ " \n",
+ " # 문장을 MAX_LEN 길이에 맞게 자르고, 모자란 부분을 패딩 0으로 채움\n",
+ " input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\n",
+ "\n",
+ " # 어텐션 마스크 초기화\n",
+ " attention_masks = []\n",
+ "\n",
+ " # 어텐션 마스크를 패딩이 아니면 1, 패딩이면 0으로 설정\n",
+ " # 패딩 부분은 BERT 모델에서 어텐션을 수행하지 않아 속도 향상\n",
+ " for seq in input_ids:\n",
+ " seq_mask = [float(i>0) for i in seq]\n",
+ " attention_masks.append(seq_mask)\n",
+ "\n",
+ " # 데이터를 파이토치의 텐서로 변환\n",
+ " inputs = torch.tensor(input_ids)\n",
+ " masks = torch.tensor(attention_masks)\n",
+ "\n",
+ " return inputs, masks\n",
+ "\n",
+ "\n",
+ "# 문장 테스트\n",
+ "def test_sentences(sentences):\n",
+ "\n",
+ " # 평가모드로 변경\n",
+ " model.eval()\n",
+ "\n",
+ " # 문장을 입력 데이터로 변환\n",
+ " inputs, masks = convert_input_data(sentences)\n",
+ "\n",
+ " # 데이터를 GPU에 넣음\n",
+ " b_input_ids = inputs.to(device)\n",
+ " b_input_mask = masks.to(device)\n",
+ " \n",
+ " # 그래디언트 계산 안함\n",
+ " with torch.no_grad(): \n",
+ " # Forward 수행\n",
+ " outputs = model(b_input_ids, \n",
+ " token_type_ids=None, \n",
+ " attention_mask=b_input_mask)\n",
+ "\n",
+ " # 로스 구함\n",
+ " logits = outputs[0]\n",
+ "\n",
+ " # CPU로 데이터 이동\n",
+ " logits = logits.detach().cpu().numpy()\n",
+ "\n",
+ " return logits"
+ ],
+ "execution_count": 82,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZQezr0tljJlM"
+ },
+ "source": [
+ "import openpyxl\n",
+ "\n",
+ "def labeling(inputName):\n",
+ " sentences = pd.read_excel(inputName+'_data.xlsx', header = None)\n",
+ " workbook = openpyxl.load_workbook(inputName+'_data.xlsx')\n",
+ " sheet = workbook['Sheet1']\n",
+ "\n",
+ " result_label = []\n",
+ " for i in range(len(sentences)):\n",
+ " logits = test_sentences([sheet.cell(column=1, row=i+1).value])\n",
+ " result_label.append(np.argmax(logits))\n",
+ " #print(np.argmax(logits))\n",
+ " print(result_label)\n",
+ " df = pandas.DataFrame(result_label)\n",
+ " df.to_excel(inputName+'_label.xlsx', header=False, index=None)\n",
+ " return result_label"
+ ],
+ "execution_count": 83,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-9MQ0SK0jofN"
+ },
+ "source": [
+ "# logits = test_sentences(['인가니 가장 큰 공포를 느낀다는 시빌미터에서 뛰어내리는 모형탑훈련 제가 직접 한번 해보겠습니다 이야야야야야야야야 쯔아크아아아아아아아가아가아아갸갸'])\n",
+ "# print(logits)\n",
+ "# print(np.argmax(logits))"
+ ],
+ "execution_count": 84,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "result_label = labeling(str(i))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zp_p-rKb3ZQW",
+ "outputId": "756b2fcf-9a1d-41b5-eb5a-574d5aa821df"
+ },
+ "execution_count": 85,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# 3. ANALYTICAL PROCESSING"
+ ],
+ "metadata": {
+ "id": "qpnB8P64Hxij"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def prediction(count_like, result_label): \n",
+ " count_like_label = result_label.count(1)\n",
+ " count_dislike_label = result_label.count(0)\n",
+ " print(\"count_like_label: \", count_like_label)\n",
+ " print(\"count_dislike_label: \", count_dislike_label)\n",
+ " print(\"댓글 좋싫비: \", count_like_label / (count_like_label + count_dislike_label))\n",
+ "\n",
+ " count_like = int(count_like)\n",
+ " print(\"count_like: \", count_like)\n",
+ " count_estimated_dislike = count_like * count_dislike_label / count_like_label / 17.7\n",
+ " print(\"count_estimated_dislike: \", int(count_estimated_dislike))"
+ ],
+ "metadata": {
+ "id": "0n8frvJuH3Q2"
+ },
+ "execution_count": 107,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "prediction(count_like, result_label)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "uPQqLf9I3Jo2",
+ "outputId": "94da27ea-7a2c-4a10-ad77-d6952b39e799"
+ },
+ "execution_count": 87,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "count_like_label: 160\n",
+ "count_dislike_label: 50\n",
+ "count_like: 19846\n",
+ "count_estimated_dislike: 6201\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# 4. 통합 실행"
+ ],
+ "metadata": {
+ "id": "c4mXG2_hU_qJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "videoIds = [\"gSIQM6cipNs\", \"umG9aF-1BmM\", \"O7oVpVfpBvE\", \"gzIkbqA7p9k\", \"TRvUe_SRJ6g\", \"elGcWzBx0fQ\", \"Zq-taY1u7fw\", \"XkcOfZ83wrk\", \"R6OyrTHPrLY\", \"iGd7f1-ONSM\", \"Lcm0xqBgfRM\"]\n",
+ "for i in range(len(videoIds)):\n",
+ " count_like = collection(videoIds[i], str(i))\n",
+ " result_label = labeling(str(i))\n",
+ " prediction(count_like, result_label)\n",
+ "\n",
+ "# 실제 싫어요:댓글 싫어요= 17.70:1 정도"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "cTzf-NxHT78b",
+ "outputId": "af02f41c-fd35-484f-b21b-39f4cd77718f"
+ },
+ "execution_count": 108,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "==========\n",
+ "title: 펄어비스 도깨비 - 첫 공개 게임플레이\n",
+ "view: 1649177\n",
+ "like: 19846 / dislike: 444\n",
+ "1000\n",
+ "실제 좋싫비: 0.9781172991621488\n",
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+ "count_like_label: 506\n",
+ "count_dislike_label: 494\n",
+ "댓글 좋싫비: 0.506\n",
+ "count_like: 19846\n",
+ "count_estimated_dislike: 1094\n",
+ "\n",
+ "==========\n",
+ "title: [배드워즈] 럭키블럭판 돌잡이 챌린지! / 로블록스\n",
+ "view: 142061\n",
+ "like: 1868 / dislike: 68\n",
+ "438\n",
+ "실제 좋싫비: 0.9648760330578512\n",
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+ "\n",
+ "==========\n",
+ "title: 독가스 방구가 몰려온다! 마을을 지켜라! -캐릭온 마크 애니\n",
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+ "\n",
+ "==========\n",
+ "title: [롤]응답하라 2018 (feat.PTSD)\n",
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+ "count_estimated_dislike: 209\n",
+ "\n",
+ "==========\n",
+ "title: 킹피스 요루와 견문색을 얻고 \"1200렙대 짱먹은\" 허기워기!!! 이것이 요루의 주인이다!!(킹피스 9화)\n",
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+ "\n",
+ "==========\n",
+ "title: 나무와 나무꾼\n",
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+ "\n",
+ "==========\n",
+ "title: 감스트 돌아온 김인직 감독되다! 1편 맨유감독 그리고 호날두 피파22\n",
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+ "count_estimated_dislike: 204\n",
+ "\n",
+ "==========\n",
+ "title: [태번 마스터] 선술집 사장님이 되어 왕실 최고의 여관 만들기🍺 (※시간순삭) (Tavern Master)\n",
+ "view: 294484\n",
+ "like: 2225 / dislike: 99\n",
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+ "실제 좋싫비: 0.9574010327022375\n",
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+ "댓글 좋싫비: 0.6784660766961652\n",
+ "count_like: 2225\n",
+ "count_estimated_dislike: 59\n",
+ "\n",
+ "==========\n",
+ "title: 끝장전 끝판왕들간의 숨막히는 한판 승부 ! 박상현 Soma vs 이재호 Light 스타 끝장전 sc1-120\n",
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+ "96\n",
+ "실제 좋싫비: 0.9545859305431879\n",
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+ "count_dislike_label: 45\n",
+ "댓글 좋싫비: 0.53125\n",
+ "count_like: 1072\n",
+ "count_estimated_dislike: 53\n",
+ "\n",
+ "==========\n",
+ "title: 제작자도 모르는 루트를 찾아낸 고인물들 진짜 미쳤네ㄷㄷ\n",
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+ "count_dislike_label: 123\n",
+ "댓글 좋싫비: 0.6144200626959248\n",
+ "count_like: 7489\n",
+ "count_estimated_dislike: 265\n",
+ "\n",
+ "==========\n",
+ "title: 25년 스토리를 일단락.. 드디어 밝혀진 검은조직의 정체!! (코난 스토리 #3)\n",
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+ "댓글 좋싫비: 0.723\n",
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+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file