diff --git a/README.md b/README.md deleted file mode 100644 index 80d51fa7..00000000 --- a/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Euron 3기 예습·복습 과제 제출 - -### ▶ [Computer Vision](https://github.com/Ewha-Euron/2022-2-Euron-CV) -### ▶ [Natural Language Processing](https://github.com/Ewha-Euron/2022-2-Euron-NLP) -### ▶ [Data Science](https://github.com/Ewha-Euron/2022-2-Euron-DS) - -## Curriculum - -| 주차 | 날짜 | CV | NLP | DS | -|---|---|---|---|---| -|1|22/08/30|cs231n 1주차|cs224n 1강|파이썬 머신러닝 완벽가이드 1~3장| -|2|22/09/06|cs231n 2주차|cs224n 2강|파이썬 머신러닝 완벽가이드 4장(1)| -|3|22/09/13|cs231n 3주차|cs224n 3강|파이썬 머신러닝 완벽가이드 4장(2)| -|4|22/09/20|cs231n 4주차|cs224n 4강|4장 관련 필사| -|5|22/09/27|cs231n 5주차|cs224n 5강|파이썬 머신러닝 완벽가이드 5장| -|6|22/10/04|cs231n 6주차|cs224n 6강|5장 관련 필사| -|7|22/10/11|중간고사 휴식기간|중간고사 휴식기간| -|8|22/10/18|중간고사 휴식기간|중간고사 휴식기간| -|9|22/10/25|cs231n 7주차|cs224n 7강|파이썬 머신러닝 완벽가이드 7장| -|10|22/11/01|논문 스터디 1|cs224n 9강|6장 관련 필사| -|11|22/11/08|cs231n 9주차|cs224n 10강|파이썬 머신러닝 완벽가이드 6장| -|12|22/11/15|cs231n 10주차|cs224n 11강|7장 관련 필사| -|13|22/11/22|cs231n 11주차|cs224n 12강|파이썬머신러닝완벽가이드 8장(1) -|14|22/11/29|cs231n 12주차|cs224n 13강|파이썬머신러닝완벽가이드 8장(2) -|15|22/12/06|기말고사 휴식기간|기말고사 휴식기간| -|16|22/12/13|기말고사 휴식기간|기말고사 휴식기간| -|17|22/12/20|cs231n 13주차|cs224n 14강|파이썬머신러닝완벽가이드 9장 -|18|22/12/27|cs231n 14주차|cs224n 15강|| -|19|22/1/3|논문 스터디 2|cs224n 18강|| -|20|22/1/10|논문 스터디 3|cs224n Low Resource Machine Translation|| -|21|22/1/17|프로젝트 아이데이션|cs224n BERT and Other Pre-trained Language Models|| -|22|22/1/24|프로젝트 기간|cs224n Guest Lecture: Scaling Language Models|| diff --git a/Week1/Gensim word vector visualization.ipynb b/Week1/Gensim word vector visualization.ipynb new file mode 100644 index 00000000..f72ded37 --- /dev/null +++ b/Week1/Gensim word vector visualization.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fc0a5260", + "metadata": {}, + "source": [ + "# Gensim word vector visualization of various word vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1170942e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: gensim in /Users/songhyejun/conda/envs/Hello/lib/python3.8/site-packages (4.2.0)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /Users/songhyejun/conda/envs/Hello/lib/python3.8/site-packages (from gensim) (6.0.0)\n", + "Requirement already satisfied: numpy>=1.17.0 in /Users/songhyejun/conda/envs/Hello/lib/python3.8/site-packages (from gensim) (1.23.1)\n", + "Requirement already satisfied: scipy>=0.18.1 in /Users/songhyejun/conda/envs/Hello/lib/python3.8/site-packages (from gensim) (1.8.1)\n", + "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "! pip install gensim" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "07bc0995", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "\n", + "from sklearn.decomposition import PCA\n", + "\n", + "import gensim.downloader as api\n", + "from gensim.models import KeyedVectors" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "477e9efa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[==================================================] 100.0% 128.1/128.1MB downloaded\n", + "\n" + ] + } + ], + "source": [ + "model = api.load(\"glove-wiki-gigaword-100\")\n", + "print(type(model))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "668bf394", + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.66146 , 0.94335 , -0.72214 , 0.17403 , -0.42524 ,\n", + " 0.36303 , 1.0135 , -0.14802 , 0.25817 , -0.20326 ,\n", + " -0.64338 , 0.16632 , 0.61518 , 1.397 , -0.094506 ,\n", + " 0.0041843, -0.18976 , -0.55421 , -0.39371 , -0.22501 ,\n", + " -0.34643 , 0.32076 , 0.34395 , -0.7034 , 0.23932 ,\n", + " 0.69951 , -0.16461 , -0.31819 , -0.34034 , -0.44906 ,\n", + " -0.069667 , 0.35348 , 0.17498 , -0.95057 , -0.2209 ,\n", + " 1.0647 , 0.23231 , 0.32569 , 0.47662 , -1.1206 ,\n", + " 0.28168 , -0.75172 , -0.54654 , -0.66337 , 0.34804 ,\n", + " -0.69058 , -0.77092 , -0.40167 , -0.069351 , -0.049238 ,\n", + " -0.39351 , 0.16735 , -0.14512 , 1.0083 , -1.0608 ,\n", + " -0.87314 , -0.29339 , 0.68278 , 0.61634 , -0.088844 ,\n", + " 0.88094 , 0.099809 , -0.27161 , -0.58026 , 0.50364 ,\n", + " -0.93814 , 0.67576 , -0.43124 , -0.10517 , -1.2404 ,\n", + " -0.74353 , 0.28637 , 0.29012 , 0.89377 , 0.67406 ,\n", + " 0.86422 , -0.30693 , -0.14718 , 0.078353 , 0.74013 ,\n", + " 0.32658 , -0.052579 , -1.1665 , 0.87079 , -0.69402 ,\n", + " -0.75977 , -0.37164 , -0.11887 , 0.18551 , 0.041883 ,\n", + " 0.59352 , 0.30519 , -0.54819 , -0.29424 , -1.4912 ,\n", + " -1.6548 , 0.98982 , 0.27325 , 1.009 , 0.94544 ],\n", + " dtype=float32)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model['bread']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b8bfc858", + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('canada', 0.6544383764266968),\n", + " ('america', 0.645224392414093),\n", + " ('u.s.a.', 0.6184033751487732),\n", + " ('united', 0.6017189621925354),\n", + " ('states', 0.5970699191093445),\n", + " ('australia', 0.5838716626167297),\n", + " ('world', 0.5590084195137024),\n", + " ('2010', 0.558070182800293),\n", + " ('2012', 0.5504006743431091),\n", + " ('davis', 0.5464468002319336)]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.most_similar('usa')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cf083b1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5a2fea32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('shunichi', 0.49618101119995117),\n", + " ('ieronymos', 0.4736502170562744),\n", + " ('pengrowth', 0.4668096601963043),\n", + " ('höss', 0.4636845886707306),\n", + " ('damaskinos', 0.4617849290370941),\n", + " ('yadin', 0.4617374837398529),\n", + " ('hundertwasser', 0.4588957726955414),\n", + " ('ncpa', 0.4577339291572571),\n", + " ('maccormac', 0.4566109776496887),\n", + " ('rothfeld', 0.4523947536945343)]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.most_similar(negative='banana')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "41650af7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "queen 0.7698541283607483\n" + ] + } + ], + "source": [ + "result = model.most_similar(positive=['woman','king'], negative=['man'])\n", + "print(*result[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d5ac6041", + "metadata": {}, + "outputs": [], + "source": [ + "def analogy(x1,x2, y1):\n", + " result = model.most_similar(positive=[y1,x2], negative=[x1])\n", + " return result[0][0]" + ] + }, + { + "attachments": { + "image-2.png": { + "image/png": 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안내된 링크에 맞추어 **직접 코드를 따라 치면서 (필사)** 해당 nlp task 의 기본적인 라이브러리와 메서드를 숙지해보시면 좋을 것 같습니다😊 필수라고 체크한 부분은 과제에 반드시 포함시켜주시고, 선택으로 체크한 부분은 자율적으로 스터디 하시면 됩니다.\n", + "\n", + "📌 궁금한 사항은 깃허브 이슈나, 카톡방, 세션 발표 시작 이전 시간 등을 활용하여 자유롭게 공유해주세요!" + ], + "metadata": { + "id": "BX3ac8Ag1RPC" + } + }, + { + "cell_type": "markdown", + "source": [ + "🥰 **이하 예제를 실습하시면 됩니다.**" + ], + "metadata": { + "id": "Kq8aMYKGPQR0" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### 🔹 BERT\n", + "\n", + "* 아래 두 예제로 BERT의 작동을 이해해 보세요!\n", + "\n", + "📌 [한국어 BERT의 마스크드 언어 모델(Masked Language Model) 실습](https://wikidocs.net/152922)\n", + "\n", + "📌 [한국어 BERT의 다음 문장 예측(Next Sentence Prediction)](https://wikidocs.net/156774)\n", + "\n", + "--- \n", + "\n", + "참고: [버트(Bidirectional Encoder Representations from Transformers, BERT)](https://wikidocs.net/115055)" + ], + "metadata": { + "id": "hbsaYOqdWMFA" + } + }, + { + "cell_type": "markdown", + "source": [ + "### 한국어 BERT의 마스크드 언어 모델(Masked Language Model) 실습\n" + ], + "metadata": { + "id": "GY7eELbNltld" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "! pip install transformers" + ], + "metadata": { + "id": "bMjBPhFgYZE6", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e2f37318-7525-4e9d-c68e-a6cde9ddba95" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting transformers\n", + " Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m71.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from 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requests->transformers) (2.10)\n", + "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\n", + "Installing collected packages: tokenizers, huggingface-hub, transformers\n", + "Successfully installed huggingface-hub-0.11.1 tokenizers-0.13.2 transformers-4.25.1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 1. masked 언어 모델과 토크나이저\n", + "from transformers import TFBertForMaskedLM\n", + "from transformers import AutoTokenizer\n", + "\n", + "model = TFBertForMaskedLM.from_pretrained('klue/bert-base', from_pt=True)\n", + "tokenizer = AutoTokenizer.from_pretrained(\"klue/bert-base\")\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 316, + "referenced_widgets": [ + "f3d0f9f546e04247a0497e1912761e53", + "ac90bceaab2d495baed3db4db6d3a1eb", + "5572ddb633ba4d02b7e52425d87ba74b", + "06e0b89fd536408bbaa53b2843c5df82", + 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