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[แ
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ฎแแ
ณ]_Prompt_Generation (1).ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {
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"collapsed": true,
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"id": "iWfIgUnFg-N6"
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},
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"outputs": [],
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"source": [
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"companies = [\"Google\", \"Meta\", \"Amazon\", \"Apple\", \"Tesla\", \"SpaceX\", \"OpenAI\", \"Perplexity\", \"Microsoft\", \"Netflix\", \"Nvidia\"]\n",
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"career_stages = [\"Junior\", \"Mid-level\", \"Senior\", \"Staff\"]\n",
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"cs_job_types = [\"Software Engineer\", \"Backend Engineer\", \"Frontend Developer\", \"Full Stack Engineer\", \"Machine Learning Researcher\", \"Data Scientist\", \"AI Engineer\", \"DevOps Engineer\", \"Cloud Infrastructure Engineer\", \"Mobile App Developer\", \"Embedded Systems Engineer\", \"Computer Vision Engineer\", \"Natural Language Processing Engineer\", \"Data Engineer\", \"Site Reliability Engineer (SRE)\", \"Database Administrator\", \"Security Engineer\", \"Quality Assurance Engineer (QA)\", \"Systems Engineer\", \"Robotics Engineer\", \"Game Developer\", \"AR/VR Engineer\", \"UI/UX Engineer\", \"Big Data Engineer\", \"Network Engineer\", \"IoT Engineer\", \"Blockchain Engineer\", \"Technical Program Manager\", \"Software Architect\", \"Research Scientist\"]"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import uuid\n",
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"\n",
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"rows = []\n",
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"\n",
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"for job_type in cs_job_types:\n",
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" for career_stage in career_stages:\n",
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" for company in companies:\n",
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" curr_type_id = str(uuid.uuid4())\n",
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" curr_type_str = f\"{job_type}_{career_stage}_{company}\"\n",
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" resume_prompt = f\"{company}์์ ์ผํ๋ {career_stage} {job_type}์ ํ์ค์ ์ธ ์ด๋ ฅ์๋ฅผ ์์ฑํ๋, '์์', '[์ด๋ฆ]'๊ณผ ๊ฐ์ ์ผ๋ฐ์ ์ธ ํํ์ด๋ ์๋ฆฌ ํ์์๋ ์ฌ์ฉํ์ง ๋ง์ธ์.\\n์ด๋ ฅ์:\"\n",
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" jd_prompt = f\"{company}์ {career_stage} {job_type} ์ฑ์ฉ ๊ณต๊ณ ๋ฅผ ์ค์ ์ฒ๋ผ ์์ฑํ๋, ์ผ๋ฐ์ ์ธ ๋ฌธ๊ตฌ๋ ์์๋ ์ฌ์ฉํ์ง ๋ง๊ณ , ๊ตฌ์ฒด์ ์ด๊ณ ์ ๋ฌธ์ ์ธ ์ด์กฐ๋ฅผ ์ ์งํ์ธ์.\\n์ฑ์ฉ ๊ณต๊ณ :\"\n",
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" rows.append([curr_type_id, curr_type_str, resume_prompt, jd_prompt])"
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],
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"metadata": {
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"collapsed": true,
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"id": "spYKmpjjh19J"
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},
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"execution_count": 46,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"import csv\n",
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"with open(\"dataset_creation_prompts.csv\", \"w\", newline=\"\", encoding=\"utf-8-sig\") as csvfile:\n",
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" writer = csv.writer(csvfile)\n",
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" writer.writerow([\"curr_type_id\", \"curr_type_str\", \"resume_prompts\", \"jd_prompts\"])\n",
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" writer.writerows(rows)"
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],
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"metadata": {
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"id": "YCChD6NQWmhR"
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},
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"execution_count": 47,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"number of type: j\n",
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"\n",
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"number of exp level: e\n",
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"\n",
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"number of company: c\n",
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"\n",
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"\n",
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"==> total positive combinations: (j X e) X C^2\n",
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"\n",
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"\n",
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"j = 30, e = 4, c = 10\n",
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"\n",
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"(30 X 4) X 100 = 120 X 100 = 12000 pairs\n",
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"\n",
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"\n",
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"total positive pairs: 12000\n",
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"total negative pairs: 12000\n",
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"\n",
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"\n",
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"training with total 24000 pairs\n"
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],
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"metadata": {
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"id": "hp7C9QIMVQAj"
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}
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}
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]
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}
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dataset_creation_prompts (1).csv
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