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+{"cells":[{"cell_type":"markdown","metadata":{"id":"giIKZavhbhOn"},"source":["# Behind the pipeline (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"BOHmqSSmbhOs"},"source":["Install the Transformers, Datasets, and Evaluate libraries to run this notebook."]},{"cell_type":"code","execution_count":2,"metadata":{"id":"K8ZhhL0kbhOt","colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779168824837,"user_tz":-540,"elapsed":4111,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"bcc1bd3a-f723-4d90-cec5-ab1e29510a5c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Requirement already satisfied: evaluate in /usr/local/lib/python3.12/dist-packages (0.4.6)\n","Requirement already satisfied: transformers==4.41.2 in /usr/local/lib/python3.12/dist-packages (from transformers[sentencepiece]==4.41.2) (4.41.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (3.29.0)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (0.36.2)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (26.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (6.0.3)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2025.11.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2.32.4)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (0.19.1)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (0.7.0)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers==4.41.2->transformers[sentencepiece]==4.41.2) (4.67.3)\n","Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.12/dist-packages (from transformers[sentencepiece]==4.41.2) (0.2.1)\n","Requirement already satisfied: protobuf in /usr/local/lib/python3.12/dist-packages (from transformers[sentencepiece]==4.41.2) (5.29.6)\n","Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (18.1.0)\n","Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (0.3.8)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from datasets) (2.2.2)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from datasets) (3.6.0)\n","Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.12/dist-packages (from datasets) (0.70.16)\n","Requirement already satisfied: fsspec<=2025.3.0,>=2023.1.0 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2025.3.0)\n","Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (3.13.5)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (1.4.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (4.15.0)\n","Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (3.4.7)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (3.13)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2.5.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2026.4.22)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2026.1)\n","Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2.6.1)\n","Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.4.0)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (26.1.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.8.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (6.7.1)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.4.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.23.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n"]}],"source":["!pip install datasets evaluate transformers[sentencepiece]==4.41.2"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"P6da2t4vbhOt","outputId":"51c7f91a-b243-47a0-d150-739d64a5b5af","colab":{"base_uri":"https://localhost:8080/","height":385,"referenced_widgets":["e3bfe3ab72584d6982fbd8a6b00c8831","d6eaa4e6b37d424ead83f5e92ab6c5a9","1a827b8bb72a4adea45d9217cfaac11e","b55116e0b10249499b560e0ab101d59c","9d3cb3fac5814a889b9f38aee5ae74a9","5bd98ae6f70c47a98d50e781361298de","54277f2e3b03481ca307c2e526ac0a3f","824941a9b6d54a26a7c7a09ee7435e1d","0abdf724c84e4d33859b68075f3f13c8","d4cabfbfd7724234b77f1f3d3a79f881","5c7fe1bf589c48eba12d16400d8e0ebb","e97faa2b18464c3dac8bf3b1080768da","c7161dead81746959908694b37ee77d7","246227fa7c8e4e1cbcd3edad6b7f6795","1595f88649204d3091df80c22161dd15","5d6e8cdc1388471883686c7654e69a82","258ba80ad7fa43b48b775d1c8954c055","cf7f167f282743c2a7b5702f84f0b000","d602d9b2d226429c89adcb0c0966512a","8fd790543ff14a41ab0135cf9be689a1","31b83c96f2304d0cb9b332af5be2a4ce","7010b2025c5748fcafed1ea8a8c100a3","313c23c2f3f14d869e78174f8f3a93eb","9b1fa551d2ab4321b0aefc8a70044e47","197234f4d242409f9dc8b55b30f2a58b","91954af4defe4a8b905983ae2214eb6b","303cc5d43a20429b85b73443076ab5cd","b7d9bb5bf4bb48108989750cf3cafc89","6b1e395439a8427fb612407f197bbd95","c9cb2770dc864673b3c8a70b9f65a6e3","9c86eb3075904f008c2e81c1623fdfc1","f9904a0564c64dee8ca728c9a5196718","f468bc23ca144994b5b55d68e89ae900","553b581c30724d1192df9c8499c632c1","28b13e3ff4974fbaafb8d12d74102674","6b740ea1ea694a619b05ae88a68dce33","83fea5fbd9b14ad9b494b9b94fda85a8","3c2e970496bf4cfd9b82c12cac62f11d","729d2d20a94f4ae299f43ce91ee589f0","f7f317c14b654d4f857d30495e62bef3","34200c9c60aa42abb828177cdc3b2d75","fbd3f5bd4db849e9a7c9b7f869dd6604","77c277febb0f48c6acdf848eb3f104e3","ccef0461a3c54bd8ac67e281452ca939"]},"executionInfo":{"status":"ok","timestamp":1779169069526,"user_tz":-540,"elapsed":20010,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e3bfe3ab72584d6982fbd8a6b00c8831"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e97faa2b18464c3dac8bf3b1080768da"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"313c23c2f3f14d869e78174f8f3a93eb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"553b581c30724d1192df9c8499c632c1"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["[{'label': 'POSITIVE', 'score': 0.9598049521446228},\n"," {'label': 'NEGATIVE', 'score': 0.9994558691978455}]"]},"metadata":{},"execution_count":3}],"source":["from transformers import pipeline\n","\n","classifier = pipeline(\"sentiment-analysis\")\n","classifier(\n","    [\n","        \"I've been waiting for a HuggingFace course my whole life.\",\n","        \"I hate this so much!\",\n","    ]\n",")"]},{"cell_type":"markdown","source":[],"metadata":{"id":"fj7KznY9voaS"}},{"cell_type":"markdown","source":["### pipeline() 의 동작 방식\n","- 1. 토크나이저 : 문장을 숫자로 변환"],"metadata":{"id":"dfMnHSxGve_K"}},{"cell_type":"code","execution_count":4,"metadata":{"id":"_UHfzFjUbhOu","colab":{"base_uri":"https://localhost:8080/","height":170,"referenced_widgets":["8f10f0a4a1d0498c82eb1ade5441d42f","c5ac475d2e60450381a98334391ecbba","34708b181ac24f1e8428e7775af800d9","e4d469505e374571873d9082a1e21dcd","a3b841f36cfa426c907fb4db01c9285b","d2bc1f2f0bd6450fa18dd84586ac63c8","d43011e0f08b4d8eb4d3024a89aba174","ba73f73c796849e4a42e1b7734774fe3","83b5c651ed22446c8139cd93196fd180","dd0077a697654174996fd67ecf292e31","9819abfbe854439f971af8a5df7e90f2","c6b950cfaf144713bd661ec765060f41","61852c98e81e443daf0916e81ff8d0f8","7e09bf84e07b44b2ad6181e84db0b46d","e1afa87bf34c425eb26da148f43e5fb4","4090d13f70f94238babd36c21ce2d43e","2d0410d3b7344c20af322a77de1d6f85","583e49fed51149b186e0a82b17deea69","3e9921387fed4c9a9ee061c4b932be9e","64259c2c6025454b809e166189b82c26","e19fa710dbe840a3b05acea8beecc26f","f3054d5a2af640468646958353da2380","9a624727a3be4b72a831539e39d97eab","4ad90ee16c5d4b89bb473cc1bfaf90b7","9bdc697d8cdd4b65ada99ee442833935","053ee5b7052f4a998158da7012986dea","832f0442bc064543b80fcc2490124bd0","cc7eeae899cc4feca3affcecbbf6cef1","ebfcfcd4a9054ee7a266d08a7bb76ad9","00230ebdbc354570adfaa839b7c35440","cabccd35575543358df0993a4a55abbe","931995f11dde46c6bf5b52aabde99184","d0a86bf5e9df46efb98a5c81873041f1"]},"executionInfo":{"status":"ok","timestamp":1779169212746,"user_tz":-540,"elapsed":394,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"0f6c4840-84f3-4049-88a0-86a4c459e6e6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8f10f0a4a1d0498c82eb1ade5441d42f"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c6b950cfaf144713bd661ec765060f41"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9a624727a3be4b72a831539e39d97eab"}},"metadata":{}}],"source":["from transformers import AutoTokenizer\n","\n","checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"IzYA-hrbbhOu","outputId":"16a070fd-f7f7-4cd4-ca9b-30ca17277024","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779169289364,"user_tz":-540,"elapsed":14,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n","          2607,  2026,  2878,  2166,  1012,   102],\n","        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n","             0,     0,     0,     0,     0,     0]]), 'attention_mask': tensor([[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, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"]}],"source":["raw_inputs = [\n","    \"I've been waiting for a HuggingFace course my whole life.\",\n","    \"I hate this so much!\",\n","]\n","\n","# tokenizer() : 문장을 토큰으로 분할\n","# padding=True : 가장 긴 시퀀스의 길이에 맞춰 패딩을 추가\n","# truncation=True : 모델이 지원하는 최대 시퀀스 길이에 맞게 잘라내기 가능\n","# return_tensors=\"pt\" : Pytorch 텐서 반환\n","inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n","print(inputs)"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"qO1MTH0ibhOv","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["b9c4b93676704fdfbe8fba8997b6a06a","d2635f90e8594c1899f713b8d2cc6d0c","ec86c3b6031447c791e915eb71f06ec3","d172f180fcf64ade9bd096183cf6f458","1c58129daac34dceaded2002da8281d0","69151d7a565c47dbbe3f88ef986b1107","114f16a27a324ad482bc953a3b872ca3","196b705664bc4f3da62b5c402ed8572e","e459d7e8e99e423d8f71c1ab2ed5b1a8","c56e2378cb464a93880466183615ab2d","f3c32997c55b427f92d3d93ebf4ea634"]},"executionInfo":{"status":"ok","timestamp":1779169635142,"user_tz":-540,"elapsed":4535,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"8639e3d7-1f3d-46eb-84cf-44cf1dd5f774"},"outputs":[{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b9c4b93676704fdfbe8fba8997b6a06a"}},"metadata":{}}],"source":["from transformers import AutoModel\n","\n","checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n","model = AutoModel.from_pretrained(checkpoint)"]},{"cell_type":"markdown","source":["- 2. 모델 삽입(벡터화된 데이터 삽입)"],"metadata":{"id":"TuJ4ghDDyX5C"}},{"cell_type":"code","execution_count":7,"metadata":{"id":"htVT-AwabhOv","outputId":"d072c9fd-0f0c-4532-df08-16a29d4ba402","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779169718098,"user_tz":-540,"elapsed":75,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([2, 16, 768])\n"]}],"source":["# 토큰화 된 값을 모델에 넣어주기\n","outputs = model(**inputs)\n","print(outputs.last_hidden_state.shape)\n","\n","# [batch size, sequence length, hidden size]\n","# torch.Size([2, 16, 768])"]},{"cell_type":"markdown","source":["- 3. 판단"],"metadata":{"id":"Jil59rYEzNFj"}},{"cell_type":"code","execution_count":8,"metadata":{"id":"s28GrkVYbhOv","executionInfo":{"status":"ok","timestamp":1779169919828,"user_tz":-540,"elapsed":451,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[],"source":["from transformers import AutoModelForSequenceClassification\n","\n","checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n","outputs = model(**inputs)"]},{"cell_type":"markdown","source":["- torch.Size([2, 2]) : 2개의 문장과 2개의 레이블(긍정, 부정) 판단"],"metadata":{"id":"T19KAikuyylZ"}},{"cell_type":"code","execution_count":9,"metadata":{"id":"hCAtrf1CbhOv","outputId":"59d86b4d-c1a9-49d3-f824-0a7fdf8716e6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779169929746,"user_tz":-540,"elapsed":7,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["torch.Size([2, 2])\n"]}],"source":["print(outputs.logits.shape)"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"l5qjVh_8bhOw","outputId":"7a42c34a-9e98-428d-c9b4-3c5617a54fad","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779170021173,"user_tz":-540,"elapsed":47,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[-1.5607,  1.6123],\n","        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward0>)\n"]}],"source":["print(outputs.logits)"]},{"cell_type":"markdown","source":["- 4. 사람이 이해하기 쉽도록 후 작업"],"metadata":{"id":"0VD8rmJozSoJ"}},{"cell_type":"code","execution_count":11,"metadata":{"id":"5d1JgjZDbhOw","outputId":"9b876c84-c007-43cd-8399-4a695b1ce362","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779170135462,"user_tz":-540,"elapsed":7,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[4.0195e-02, 9.5980e-01],\n","        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward0>)\n"]}],"source":["import torch\n","\n","predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n","print(predictions)"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"OLp47nXHbhOw","outputId":"4eff39a7-6c73-4bea-acc2-3bded25a6faf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779170139053,"user_tz":-540,"elapsed":44,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{0: 'NEGATIVE', 1: 'POSITIVE'}"]},"metadata":{},"execution_count":12}],"source":["model.config.id2label"]},{"cell_type":"markdown","source":["### 토크나이저 추가\n","\n","\"Hello\" 일때:\n","\n","| 토큰 | ID | 의미 |\n","|----|----|----|\n","| [CLS] | 101 | 문장의 시작 |\n","| hello | 7592 | Hello |\n","| [SEP] | 102 | 문장의 끝 |\n","| [PAD] | 0 | 문장 길이 맞추기용 빈칸 |\n"],"metadata":{"id":"9OH8vliUzyoJ"}},{"cell_type":"code","source":["sequence = [\n","    \"Hello\",\n","    \"Cool\",\n","    \"Nice!\"\n","]\n","\n","inputs = tokenizer(sequence, padding=True, truncation=True, return_tensors=\"pt\")\n","inputs"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AO559-uX0yFZ","executionInfo":{"status":"ok","timestamp":1779170551276,"user_tz":-540,"elapsed":22,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"ba62ce73-8dab-456d-a392-d9e5ec719511"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': tensor([[ 101, 7592,  102,    0],\n","        [ 101, 4658,  102,    0],\n","        [ 101, 3835,  999,  102]]), 'attention_mask': tensor([[1, 1, 1, 0],\n","        [1, 1, 1, 0],\n","        [1, 1, 1, 1]])}"]},"metadata":{},"execution_count":13}]}],"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/huggingface/notebooks/blob/master/course/en/chapter2/section2_pt.ipynb","timestamp":1779163871467}]},"language_info":{"name":"python"},"kernelspec":{"name":"python3","display_name":"Python 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diff --git a/colab/Fine-tuning a model with the Trainer API or Keras.ipynb b/colab/Fine-tuning a model with the Trainer API or Keras.ipynb
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+{"cells":[{"cell_type":"markdown","metadata":{"id":"sl90BFcgb3Gd"},"source":["# Fine-tuning a model with the Trainer API"]},{"cell_type":"markdown","metadata":{"id":"pZ67By2Rb3Ge"},"source":["Install the Transformers, Datasets, and Evaluate libraries to run this notebook."]},{"cell_type":"code","execution_count":1,"metadata":{"id":"Lpjg3fQhb3Gf","colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779238411789,"user_tz":-540,"elapsed":16582,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"ffe976d5-ce10-45b4-9164-469af0d2da54"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Collecting evaluate\n","  Downloading evaluate-0.4.6-py3-none-any.whl.metadata (9.5 kB)\n","Requirement already satisfied: transformers[sentencepiece] in /usr/local/lib/python3.12/dist-packages (5.0.0)\n","Requirement already 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transformers[sentencepiece]"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"2jlgj7Dcb3Gf","colab":{"base_uri":"https://localhost:8080/","height":716,"referenced_widgets":["6a1eebf6d6a840b3a394da98ff442dc6","b1c0fb308dd0478592d5deb542151b32","f5b5581d0365434db218f80c77d1fcc5","40156688efac4be98409bf33a6ca18cf","b2cb8890b3ee4e3fae4ffb83beadc67f","4f1cd0c1679746e9bdd993791b1598ea","c9948ff97860424e8f557f338365a71d","764dc3050ee8450d8eec7878b26fa9ed","0c28a6da7e044cc488296853b487478f","8a62413c5f8d49788824da23b5dcf6f6","5013b8f12fdb43f8a761417d848fac36","561cafd3bed34b7f9a26a3cfd88ab3ee","90fff3610d88484aa2a2596da7cad6d0","78ab2376c2be49df88d9e6b540d8f025","f6a256c9e5c147f4b075f8ac05d3a9b6","776b45038a3b44ef9281474e25d273f2","3b1f5ec0f3a046f6b2effcf45ebb8050","2ecac5cbbf6c473994b25d92cb8aed54","6afbd491995d4cf2b0dd3b984f73ec68","cd688df6dfa340b2ab3b0bfc643d327d","f16179c4c4044f048bebbec60c02bb47","dc35fc9e8290432aa8913e5ab2d603f9","2d1d331748454515a68ef5b0bc6a7aa9","255f24f5d8db487fa4963b88e474b0e6","7d13f0b8257a4ba9912386b7b49ecd8f","d212180869c642f68c85849409424874","ec54609a461c4318a2704503ba649b79","fe7ea5f9f33c4320a879f610af3f9760","293079ec5b1b4b518da06b367d57e97b","295a30555c954b02aee6f7e7746d0743","5e97b133261f47c8a3b2b9be1e2d7bf4","abddf5b966614b30a8f559b2d49fddf0","ddcc29fe34584b8aa42585c6b9064bf4","9231ffd0767c486cae4dde35425f6727","594074c4e02a4f4da9e3ded67e2000b2","63046ddb47c74b88a7c3781ed8055686","71031e6d403f42e684af50e7c4e5e42b","6b9386e944ee431495192d37f4cf934d","466a982ed44040c6bc4d9f6fee5d871c","4509f67986eb443790e6e41f02714d2c","f7d575de8920433ea8d3f45e529d5526","d30a822e1ba04b119e1acb21d5ec607e","716ef37ea9ed4569acd5436e302b8dfc","4d8564ca75f6448b98ad46184ec4ff6f","833b6045fc534d11a6b2a4e58a117dfa","8c2641af34684e61aec9b70f298c1a68","063a00c731964ed19384a7e34f6f16c7","e6acb98b1d394ac0955c021e9ed730d4","5516cc790bf0461ca810527a8a354349","3f28696caa894159ad24e0fe5f4075dc","a236d925dd174ee5ad497221f3aa6a4b","0d6ed5d015ee4bd18d22f9aeba582280","5ec7bd349e9e49a3aba73e5e019dc26d","58116c0fd9ee4af998554ecf8bcfd845","e4dde7c1b6254227aa0b835805cebcaf","4b31aab1fa1c4d51bfd6974c5daf65e8","f2f7c38a3470416ab4be704cc85bd08a","f20fefe327a44e4db6ff604b6892067d","b136176a273746f9ac05235b622a7f0e","28dbfbe018fa4505a2c04afc1e2fc024","23fb9ffee3404043a1813e2b089d2fb4","a921cced7ae447048bbb6dd074169f56","2cb52c34754d461681df5a7c80b7494e","c08cbb7e363b4326b99d5d92f7fba4f0","e8a945a927934b558e9cb3d9b7d7ee97","06ccdd955af14462aba3373cbf5ad827","b885c03f72e8490eb23f36538b224931","bf43d7a7503d409296c1fddf88152c01","9c8e3466af59435b98e13fc072e89a13","355f9cea5e48428db1814b293b139f90","766f92e443e6410fa16ef48a8c51448d","6c9399f650174a8a886b1f1739b8660d","09b3ee47ac444278b843a66ee794fefe","1370e09b8b0c400fbb98de21251e9545","0c3a0bcf9bba46b8a32ec5a425efe526","9bab18580d1048ddbfaacd599ad7e6b6","41482aa6d90d4a5e8a15226106f7c1d5","5ce50bc8393e46ba9cd6ea5bcbe4e925","15317e17d12e48cdb89d29a2437f80ce","46ca6db0f16d4e81afe290a95bec5b64","b03bd0ffc59345748d9b72ebb3703ac5","5f7d4c83e5bd4047ad0a6c1102702573","3e3a264e7b6a412292f6ca50f600d5e6","7fb8bb1f5ac1497ca8ad852400bca154","e44ae1339a914c04a4767dc57a06325b","6f3e8a8325154e24a08f906ddfa055b6","c4942271a24143f2bbf5c22d080f8999","ef125459b8154391bc34882f05834c99","9be3f463c18e4c329c1137fc8e7746e7","4145aa11176649e2b3a2fbdeb4de084f","f60eefccd3054929bbf37cfa05cd8924","70ee54390d004c4e8a9264bbc4139914","829dded229b04520916a4511abddc11c","4da2c4eb039143fd884fd1b297d4a984","663aa424edb14a5f8bceb3eebda8144c","8310e99a75e744e9b94a31a4994e88d6","1c4242c3baeb4854a2230b4951e349eb","3047be9baf654f39a80c5fb48c956500","2caed48c7801447481f7515c2db63062","92ad6e44a20848a7b8ef0a20072779e8","b7f0be4cf6014455821946f0eead5962","03b656122f2e4fd695effe6de43a92c1","79013733b5984a07a15ff5f7a15df17f","c10e13c114344aae88d520ca3b65372a","ca5e2ad2910f45a9ae1fe4650a28060e","7ffa36fa825249cb8533b2765f2f6f10","12400cadd2b143b797d66a5797f5dfcc","6caceacc215743a19a67a1b96b782257","a8a26295adb241ddac16120df111ee54","4d3ada4d2c144cb8bdc90d8e0c9d9258","e399ebfee5124670a0d88deef03da896","a4823e499d04437caf27ef73bc879601","60e352a03ce446749de0ae6639b74066","3a6aa1e92bdd46fba3924023652e10ca","8e1b350bc9c049e681e055c8a4e8960e","713c7ed6b533495ca27a25c07e5a1cfb","11d91e1369f0476ab822a80599c0115f","b074fb0757504a71891d876f55f8d1d6","bc94fa61aa9849c5bc038ec77f24f38b","c617532618204d80a4b6f4e62e43b2e8","f64934ad3093426382e9b9f058f9a14b","e71a709ce23c44e8907cba009dcbf2f5","6785bb467bca4066b1c7660696158240","9a675e8344d54706a1d64c349165a171","6ddb5420ac8c4a46afd62f59d918b7fc","d3adcff08b4a4dbd93ac3de2df6322fc","419afa72b20e4d0682c597c28946c35c","f298319b07854979a477cc18e7b4759a","8283393b576e4079b6b43dcc675707ff","6aab6f17ee57452aa81098706e1ff6a5","2f79319a4aa842aea60fe980bac6b664","e0a320e8d5ec44fa98de9b03e0880995","61ae762b96a74a86a6abaf7b08131237","9d45ae88bb814bdcbf72f1c52021829a","932f169ba9df4869965526f0208a2e59","68995158196348bda9a8bb2340617445","623df91d0e214965ab9ba8362b26d173","8ffeb5d9addf47339762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UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n","Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a1eebf6d6a840b3a394da98ff442dc6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["mrpc/train-00000-of-00001.parquet:   0%|          | 0.00/649k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"561cafd3bed34b7f9a26a3cfd88ab3ee"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["mrpc/validation-00000-of-00001.parquet:   0%|          | 0.00/75.7k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2d1d331748454515a68ef5b0bc6a7aa9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["mrpc/test-00000-of-00001.parquet:   0%|          | 0.00/308k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9231ffd0767c486cae4dde35425f6727"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/3668 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"833b6045fc534d11a6b2a4e58a117dfa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/408 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4b31aab1fa1c4d51bfd6974c5daf65e8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/1725 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b885c03f72e8490eb23f36538b224931"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5ce50bc8393e46ba9cd6ea5bcbe4e925"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9be3f463c18e4c329c1137fc8e7746e7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"92ad6e44a20848a7b8ef0a20072779e8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e399ebfee5124670a0d88deef03da896"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e71a709ce23c44e8907cba009dcbf2f5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/408 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"61ae762b96a74a86a6abaf7b08131237"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a8705e9c54ef42abbdf72d4cdd85ba8e"}},"metadata":{}}],"source":["from datasets import load_dataset\n","from transformers import AutoTokenizer, DataCollatorWithPadding\n","\n","raw_datasets = load_dataset(\"glue\", \"mrpc\")\n","checkpoint = \"bert-base-uncased\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","\n","\n","def tokenize_function(example):\n","    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n","\n","\n","tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n","data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"]},{"cell_type":"markdown","source":["- 학습 시 여러 단계를 거쳐서 결과가 나옴\n","- 여러 단계를 Trainer API 를 이용해 제공해 줌으로써 간단하게 처리 가능"],"metadata":{"id":"8IH7VWpi47Wb"}},{"cell_type":"code","execution_count":3,"metadata":{"id":"pk07ijukb3Gg","executionInfo":{"status":"ok","timestamp":1779238884288,"user_tz":-540,"elapsed":61,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[],"source":["from transformers import TrainingArguments\n","\n","# 학습설정 객체 생성(저장위치 설정, batch 크기, 반복횟수 지정 가능)\n","training_args = TrainingArguments(\"test-trainer\")"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"-xKQvvAFb3Gg","colab":{"base_uri":"https://localhost:8080/","height":395,"referenced_widgets":["a3cf9b6415b7443fba26e174c0b2af85","01eddd2f64314db48b7f954a96d451c7","cde34cee9fb74dd7a8fc69174d588ef5","d4161ae38c6241e1a89ab8a90d865be3","928990dd0ff14b43bb3e47ea017c8422","7818569f90ea4f34a1070cc59be891d3","7a0b3c6c1b154562b392b9fb7ea4171f","a1b67513fd3f4a06bbb2dae34c5faf41","37dfe8682a724a37bb6bc993f5af02ad","fa3a339a8d8142b6803b296ce61e1653","6091400ec8ca458dbb959206f8a4c32a","054325c1101549419ac1d61d6ba32ac3","379a4a166a2f4847abc0b2538315187e","03ab9c4f2fb44f0fbdf02ffcb6ad543f","f1fe94a0f3a648c385fc50cf882d71fd","6321552254cb4b1284366241d23fdaf9","21fa007f7c3a4144bd8004f5e2af0be6","8fbe0fabe34648eeb6bec1adb9f859ee","98bde44b489648d680583fcb7a46fdb1","b7a06073e7704b51b5b943ea8b03854f","cd672b7b105c4c42b6e81ea6df79737e","2fba4878731548fd88d47a71dac143a9"]},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779238970692,"user_tz":-540,"elapsed":13850,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"a1fd95aa-3546-4856-fbdb-c6ef793f5a68"},"outputs":[{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a3cf9b6415b7443fba26e174c0b2af85"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"054325c1101549419ac1d61d6ba32ac3"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["BertForSequenceClassification LOAD REPORT from: bert-base-uncased\n","Key                                        | Status     | \n","-------------------------------------------+------------+-\n","cls.predictions.transform.dense.weight     | UNEXPECTED | \n","cls.predictions.bias                       | UNEXPECTED | \n","cls.seq_relationship.weight                | UNEXPECTED | \n","cls.predictions.transform.LayerNorm.bias   | UNEXPECTED | \n","cls.predictions.transform.dense.bias       | UNEXPECTED | \n","cls.seq_relationship.bias                  | UNEXPECTED | \n","cls.predictions.transform.LayerNorm.weight | UNEXPECTED | \n","classifier.weight                          | MISSING    | \n","classifier.bias                            | MISSING    | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n","- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"]}],"source":["# 문장 분류용 모델\n","from transformers import AutoModelForSequenceClassification\n","\n","# num_labels : True, False 두개 사용\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"0EgKKxOFb3Gg","executionInfo":{"status":"ok","timestamp":1779239062232,"user_tz":-540,"elapsed":807,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[],"source":["# Trainer : 학습관리자\n","# 학습시 Loss, Backward, Forward, Evaluation, Save, Logging...\n","from transformers import Trainer\n","\n","trainer = Trainer(\n","    model,\n","    training_args,\n","    train_dataset=tokenized_datasets[\"train\"],\n","    eval_dataset=tokenized_datasets[\"validation\"],\n","    data_collator=data_collator,\n","    processing_class=tokenizer,\n",")"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"KuYxnVh5b3Gh","colab":{"base_uri":"https://localhost:8080/","height":318},"executionInfo":{"status":"error","timestamp":1779239512546,"user_tz":-540,"elapsed":1137,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"49263945-186f-4f93-f077-fc15df4ca58d"},"outputs":[{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_4318/865242939.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 학습시작\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2172\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2173\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2174\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   2175\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2176\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2534\u001b[0m                     )\n\u001b[1;32m   2535\u001b[0m                     \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2536\u001b[0;31m                         \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_items_in_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3811\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m   3878\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"num_items_in_batch\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m 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cache_position, **kwargs)\u001b[0m\n\u001b[1;32m    450\u001b[0m     ) -> tuple[torch.Tensor] | BaseModelOutputWithPastAndCrossAttentions:\n\u001b[1;32m    451\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_module\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 452\u001b[0;31m             hidden_states = layer_module(\n\u001b[0m\u001b[1;32m    453\u001b[0m                 \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    454\u001b[0m                 \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/modeling_layers.py\u001b[0m in 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_global_forward_pre_hooks):\n\u001b[0;32m-> 1787\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mhidden_states\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1774\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1775\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1776\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1777\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1778\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1785\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1786\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1787\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0mRuns\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mforward\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \"\"\"\n\u001b[0;32m--> 134\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["# 학습시작\n","trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QcCUmeycb3Gh","outputId":"2ad5d6ab-be3a-4144-bbde-a05912107e39"},"outputs":[{"data":{"text/plain":["(408, 2) (408,)"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["# 검증데이터를 가지고 추론 수행\n","\n","predictions = trainer.predict(tokenized_datasets[\"validation\"])\n","print(predictions.predictions.shape, predictions.label_ids.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YowQsgLAb3Gh"},"outputs":[],"source":["import numpy as np\n","# logits -> 0 or 1 판단\n","preds = np.argmax(predictions.predictions, axis=-1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HuoSDQysb3Gi","outputId":"140b8705-27f0-4c36-c8bf-889d55338ad3"},"outputs":[{"data":{"text/plain":["{'accuracy': 0.8578431372549019, 'f1': 0.8996539792387542}"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["import evaluate\n","\n","# 모델 평가\n","metric = evaluate.load(\"glue\", \"mrpc\")\n","metric.compute(predictions=preds, references=predictions.label_ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X0Lm5MxXb3Gi"},"outputs":[],"source":["def compute_metrics(eval_preds):\n","    metric = evaluate.load(\"glue\", \"mrpc\")\n","    logits, labels = eval_preds\n","    predictions = np.argmax(logits, axis=-1)\n","    return metric.compute(predictions=predictions, references=labels)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LjiRYUhpb3Gi"},"outputs":[],"source":["training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n","\n","trainer = Trainer(\n","    model,\n","    training_args,\n","    train_dataset=tokenized_datasets[\"train\"],\n","    eval_dataset=tokenized_datasets[\"validation\"],\n","    data_collator=data_collator,\n","    processing_class=tokenizer,\n","    compute_metrics=compute_metrics,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U6xUoDb3b3Gi"},"outputs":[],"source":["trainer.train()"]}],"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/huggingface/notebooks/blob/master/course/en/chapter3/section3.ipynb","timestamp":1779163957871}]},"language_info":{"name":"python"},"kernelspec":{"name":"python3","display_name":"Python 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+{"cells":[{"cell_type":"markdown","metadata":{"id":"5QYneonVbwIj"},"source":["# Processing the data (PyTorch)"]},{"cell_type":"markdown","metadata":{"id":"2uvMdtwqbwIk"},"source":["Install the Transformers, Datasets, and Evaluate libraries to run this notebook."]},{"cell_type":"code","execution_count":43,"metadata":{"id":"kxvt4_2gbwIk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174438546,"user_tz":-540,"elapsed":5702,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"b6c19692-1299-47e3-d79d-7dbfacb69786"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Requirement already satisfied: evaluate in /usr/local/lib/python3.12/dist-packages (0.4.6)\n","Requirement already satisfied: transformers==4.41.2 in /usr/local/lib/python3.12/dist-packages (from transformers[sentencepiece]==4.41.2) (4.41.2)\n","Requirement 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(from huggingface-hub<1.0,>=0.23.0->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (1.4.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.23.0->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (4.15.0)\n","Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (3.4.7)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (3.13)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2.5.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->transformers==4.41.2->transformers[sentencepiece]==4.41.2) (2026.4.22)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2026.1)\n","Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2.6.1)\n","Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.4.0)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (26.1.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.8.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (6.7.1)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.4.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.23.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n"]}],"source":["!pip install datasets evaluate transformers[sentencepiece]==4.41.2"]},{"cell_type":"markdown","source":["감정분석 (긍정, 부정), 문장 유사도, 뉴스 분류, 스팸 분류와 같은 작업을 담당하는 모델은 어덯게 훈련되는가?"],"metadata":{"id":"MZsd7_hd3riT"}},{"cell_type":"code","execution_count":30,"metadata":{"id":"m0HQpTk_bwIk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174398714,"user_tz":-540,"elapsed":15136,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"a41d72a2-bce0-4d51-a22e-6e2d582e2ff0"},"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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"]}],"source":["import torch\n","from torch.optim import AdamW\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","\n","# Same as before\n","checkpoint = \"bert-base-uncased\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n","sequences = [\n","    \"I've been waiting for a HuggingFace course my whole life.\",\n","    \"This course is amazing!\",\n","]\n","batch = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n","\n","# This is new\n","# 정답 제공\n","batch[\"labels\"] = torch.tensor([1, 1])\n","\n","# 최적화(모델이 틀린 정도를 보고 가중치를 수정)\n","optimizer = AdamW(model.parameters())\n","# 모델이 얼마나 틀렸는지\n","loss = model(**batch).loss\n","\n","# 역전파 : 수정하는 단계\n","loss.backward()\n","\n","# 가중치 업데이트\n","optimizer.step()"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"jAE4TjAFbwIk","outputId":"3bf5ed6a-aea8-412e-94c3-fb48ae98eff4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174399976,"user_tz":-540,"elapsed":1259,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['sentence1', 'sentence2', 'label', 'idx'],\n","        num_rows: 3668\n","    })\n","    validation: Dataset({\n","        features: ['sentence1', 'sentence2', 'label', 'idx'],\n","        num_rows: 408\n","    })\n","    test: Dataset({\n","        features: ['sentence1', 'sentence2', 'label', 'idx'],\n","        num_rows: 1725\n","    })\n","})"]},"metadata":{},"execution_count":31}],"source":["from datasets import load_dataset\n","\n","# glue : 자연어 처리 성능 평가용\n","# mrpc : Microsoft Research Parapharase Corpus ( 두 개의 문장이 같은 의미인가? 판단용 데이터 셋)\n","raw_datasets = load_dataset(\"glue\", \"mrpc\")\n","raw_datasets"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"GG4GDsq3bwIk","outputId":"6719590f-2d16-4cad-d3e8-925e59e3ee6f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174400001,"user_tz":-540,"elapsed":23,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n"," 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n"," 'label': 1,\n"," 'idx': 0}"]},"metadata":{},"execution_count":32}],"source":["raw_train_dataset = raw_datasets[\"train\"]\n","raw_train_dataset[0]"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"hhQRSRYVbwIk","outputId":"108da297-a6a9-4c96-8765-737b02782d16","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174400057,"user_tz":-540,"elapsed":55,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sentence1': Value('string'),\n"," 'sentence2': Value('string'),\n"," 'label': ClassLabel(names=['not_equivalent', 'equivalent']),\n"," 'idx': Value('int32')}"]},"metadata":{},"execution_count":33}],"source":["raw_train_dataset.features"]},{"cell_type":"code","execution_count":34,"metadata":{"id":"WjKiwclQbwIk","executionInfo":{"status":"ok","timestamp":1779174404707,"user_tz":-540,"elapsed":4647,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[],"source":["from transformers import AutoTokenizer\n","\n","checkpoint = \"bert-base-uncased\"\n","tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n","\n","# 컬럼별로 토큰화 하는 작업\n","tokenized_sentences_1 = tokenizer(list(raw_datasets[\"train\"][\"sentence1\"]))\n","tokenized_sentences_2 = tokenizer(list(raw_datasets[\"train\"][\"sentence2\"]))"]},{"cell_type":"code","execution_count":35,"metadata":{"id":"rv0lJYFtbwIl","outputId":"bfaccfbd-9107-4407-aa5d-7a52dbd3a15b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174404764,"user_tz":-540,"elapsed":59,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [101, 2023, 2003, 1996, 2034, 6251, 1012, 102, 2023, 2003, 1996, 2117, 2028, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"]},"metadata":{},"execution_count":35}],"source":["# 문장 여러개를 한꺼번에 토큰화\n","inputs = tokenizer(\"This is the first sentence.\", \"This is the second one.\")\n","inputs\n","\n","# 두개의 문장을 하나로 합쳐서 처리 (bert-base-uncased)"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"hLLMKSX_bwIl","outputId":"c6d22894-857d-45a9-e810-0c3eba83d7e7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174404820,"user_tz":-540,"elapsed":39,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['[CLS]',\n"," 'this',\n"," 'is',\n"," 'the',\n"," 'first',\n"," 'sentence',\n"," '.',\n"," '[SEP]',\n"," 'this',\n"," 'is',\n"," 'the',\n"," 'second',\n"," 'one',\n"," '.',\n"," '[SEP]']"]},"metadata":{},"execution_count":36}],"source":["tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"])"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"FC--VZpIbwIl","executionInfo":{"status":"ok","timestamp":1779174408865,"user_tz":-540,"elapsed":4043,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[],"source":["# 전체 문장을 넘겨서 토큰화\n","tokenized_dataset = tokenizer(\n","    list(raw_datasets[\"train\"][\"sentence1\"]),\n","    list(raw_datasets[\"train\"][\"sentence2\"]),\n","    padding=True,\n","    truncation=True,\n",")"]},{"cell_type":"code","execution_count":38,"metadata":{"id":"j-SWE4_qbwIl","executionInfo":{"status":"ok","timestamp":1779174408912,"user_tz":-540,"elapsed":44,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[],"source":["# 토큰화 함수\n","def tokenize_function(example):\n","    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"]},{"cell_type":"code","execution_count":39,"metadata":{"id":"JIFzo24XbwIp","outputId":"f0192197-3c1e-466c-d954-235d11379308","colab":{"base_uri":"https://localhost:8080/","height":306,"referenced_widgets":["ea5b2e63c5674154be2392454df44e0a","0de73b9e8ef144ff817e5b5a58fa9191","690270226e5f423fb627bd3bfd27c579","45823d2708a44a2285fcf6077997dd7d","eb3358cb2fcb449898f829e54c670936","53877b117cba499d955fb9be7b6a5c1f","253b8df3c9b94fa898ae24af7af3ef6c","7774e0384639412fac470a14712b18ac","21a01fca995f41829b7eb0e3432c888a","707facdd6f23465daff78b7ef51fab9e","0dd56654819e4891aa5fcc47baf12247"]},"executionInfo":{"status":"ok","timestamp":1779174409804,"user_tz":-540,"elapsed":872,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/408 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ea5b2e63c5674154be2392454df44e0a"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n","        num_rows: 3668\n","    })\n","    validation: Dataset({\n","        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n","        num_rows: 408\n","    })\n","    test: Dataset({\n","        features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n","        num_rows: 1725\n","    })\n","})"]},"metadata":{},"execution_count":39}],"source":["tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n","tokenized_datasets"]},{"cell_type":"code","execution_count":40,"metadata":{"id":"Iu9lPv-5bwIp","executionInfo":{"status":"ok","timestamp":1779174432804,"user_tz":-540,"elapsed":23027,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[],"source":["# padding=True : 가장 긴 시퀀스의 길이(데이터셋 전체에서)에 맞춰 패딩을 추가\n","# 위 개념 적용 시 메모리 낭비가 발생할 수도 있음\n","\n","# batch(병렬처리 - 가능한 학습 단위)\n","# DataCollatorWithPadding : 하나의 batch 안에서만 가장 긴 길이의 맞춰 padding 넣기\n","\n","from transformers import DataCollatorWithPadding\n","\n","data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"]},{"cell_type":"code","execution_count":41,"metadata":{"id":"vS9GD3W4bwIp","outputId":"3e881b1f-3778-4273-8b68-e91d66adf076","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174432848,"user_tz":-540,"elapsed":23,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[50, 59, 47, 67, 59, 50, 62, 32]"]},"metadata":{},"execution_count":41}],"source":["samples = tokenized_datasets[\"train\"][:8]\n","samples = {k: v for k, v in samples.items() if k not in [\"idx\", \"sentence1\", \"sentence2\"]}\n","[len(x) for x in samples[\"input_ids\"]]"]},{"cell_type":"code","execution_count":42,"metadata":{"id":"bKi1a_EObwIp","outputId":"6cf70b02-b386-4790-daf9-8cdac78d61bc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779174432849,"user_tz":-540,"elapsed":15,"user":{"displayName":"쿠니","userId":"18312388301484735023"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': torch.Size([8, 67]),\n"," 'token_type_ids': torch.Size([8, 67]),\n"," 'attention_mask': torch.Size([8, 67]),\n"," 'labels': torch.Size([8])}"]},"metadata":{},"execution_count":42}],"source":["batch = data_collator(samples)\n","{k: v.shape for k, v in batch.items()}"]}],"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/huggingface/notebooks/blob/master/course/en/chapter3/section2.ipynb","timestamp":1779163929829}]},"language_info":{"name":"python"},"kernelspec":{"name":"python3","display_name":"Python 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\ No newline at end of file
diff --git a/colab/Untitled b/colab/Untitled
new file mode 100644
index 0000000..e3d80dd
--- /dev/null
+++ b/colab/Untitled
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Untitled","authorship_tag":"ABX9TyOw7VNSeqhRP+eOellpIRQg"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":0,"metadata":{},"outputs":[],"source":[""]}]}
\ No newline at end of file
diff --git a/colab/Untitled0.ipynb b/colab/Untitled0.ipynb
new file mode 100644
index 0000000..db26e5f
--- /dev/null
+++ b/colab/Untitled0.ipynb
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMa7ybUWuUj1+ud5HMXowhK"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oKfpiooC3aQx","executionInfo":{"status":"ok","timestamp":1778557421821,"user_tz":-540,"elapsed":10,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"778aef8d-2a09-4c71-b3f0-75c961c340e4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Hello\n"]}],"source":["print(\"Hello\")"]}]}
\ No newline at end of file
diff --git a/colab/Untitled1.ipynb b/colab/Untitled1.ipynb
new file mode 100644
index 0000000..3332a96
--- /dev/null
+++ b/colab/Untitled1.ipynb
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOuI/HFRXnL2TxCZhFMJvEF"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"fP-707OLlJF-"},"outputs":[],"source":["!pip install transformers[sentencepiece]==4.41.2 torch gradiO python-dotenv"]},{"cell_type":"code","source":["import gradio as gr\n","from transformers import pipeline\n","from PIL import Image\n","\n","captioner = pipeline(\"image-to-text\")\n","\n","def echo(message, history) :\n","\n","    # message {'text': 'text', 'files': []}\n","    # message {'text': '', 'files': ['/private/var/folders/_x/jfhn2s8d6t512jvv5lndzw2m0000gn/T/gradio/f299e1f235318c3c17f47efef61fabb3f52e14158f3c4e1a2f15b1cf0fb8329d/Gemini_Generated_Image_e9k4qje9k4qje9k4.png']}\n","\n","    # History[]\n","    # History[{'role': 'user', 'metadata': None, 'content': [{'text': 'text', 'type': 'text'}], 'options': None}, {'role': 'assistant', ']\n","    print(\"message\", message)\n","    print(\"history\", history)\n","\n","    # message에서 텍스트와 이미지 분리\n","    text = message[\"text\"]\n","\n","    if message.get(\"files\") :\n","        image = message[\"files\"][0]\n","        if image :\n","            # 이미지만\n","            result = captioner(image)\n","            return result[0]['generated_text']\n","    elif text :\n","        return text\n","\n","\n","\n","demo = gr.ChatInterface(\n","    fn=echo,\n","    multimodal=True,\n","    title=\"멀티 모달 AI 챗봇\",\n","    description=\"이미지를 업로드하면 이미지에 대한 설명을 생성하는 챗봇입니다. 테스트로 질문도 가능합니다.\",\n",")\n","demo.launch()"],"metadata":{"id":"XbKK4cPklO8T"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import gradio as gr\n","from transformers import pipeline\n","from PIL import Image\n","\n","captioner = pipeline(\"image-to-text\")\n","\n","def chat(message, history) :\n","\n","    # message {'text': 'text', 'files': []}\n","    # message {'text': '', 'files': ['/private/var/folders/_x/jfhn2s8d6t512jvv5lndzw2m0000gn/T/gradio/f299e1f235318c3c17f47efef61fabb3f52e14158f3c4e1a2f15b1cf0fb8329d/Gemini_Generated_Image_e9k4qje9k4qje9k4.png']}\n","\n","    # History[]\n","    # History[{'role': 'user', 'metadata': None, 'content': [{'text': 'text', 'type': 'text'}], 'options': None}, {'role': 'assistant', ']\n","    print(\"message\", message)\n","    print(\"history\", history)\n","\n","    # message에서 텍스트와 이미지 분리\n","    text = message[\"text\"]\n","\n","    if message.get(\"files\") :\n","        image = message[\"files\"][0]\n","        if image :\n","            result = captioner(image)\n","            caption_result = result[0][\"generated_text\"]\n","            prompt = f\"\"\"\n","            이미지 설명:\n","            {caption_result}\n","\n","            사용자 질문:\n","            {text}\n","            \"\"\"\n","            return prompt\n","    elif text :\n","        return text\n","\n","\n","\n","demo = gr.ChatInterface(\n","    fn=chat,\n","    multimodal=True,\n","    title=\"멀티 모달 AI 챗봇\",\n","    description=\"이미지를 업로드하면 이미지에 대한 설명을 생성하는 챗봇입니다. 테스트로 질문도 가능합니다.\",\n",")\n","demo.launch()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":428},"id":"fuTWrcWQ9HyW","executionInfo":{"status":"error","timestamp":1779256780357,"user_tz":-540,"elapsed":44195,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"9ff30f5c-eaff-4e1c-85bf-dde225532400"},"execution_count":1,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"\"Unknown task image-to-text, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']\"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_4438/1356788246.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mPIL\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcaptioner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"image-to-text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mchat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mpipeline\u001b[0;34m(task, model, config, tokenizer, feature_extractor, image_processor, processor, revision, use_fast, token, device, device_map, dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[1;32m    775\u001b[0m             )\n\u001b[1;32m    776\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m         \u001b[0mnormalized_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtask_options\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpipeline_class\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m             \u001b[0mpipeline_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtargeted_task\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"impl\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/__init__.py\u001b[0m in \u001b[0;36mcheck_task\u001b[0;34m(task)\u001b[0m\n\u001b[1;32m    379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m     \"\"\"\n\u001b[0;32m--> 381\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mPIPELINE_REGISTRY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/pipelines/base.py\u001b[0m in \u001b[0;36mcheck_task\u001b[0;34m(self, task)\u001b[0m\n\u001b[1;32m   1354\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Invalid translation task {task}, use 'translation_XX_to_YY' format\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m         raise KeyError(\n\u001b[0m\u001b[1;32m   1357\u001b[0m             \u001b[0;34mf\"Unknown task {task}, available tasks are {self.get_supported_tasks() + ['translation_XX_to_YY']}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m         )\n","\u001b[0;31mKeyError\u001b[0m: \"Unknown task image-to-text, available tasks are ['any-to-any', 'audio-classification', 'automatic-speech-recognition', 'depth-estimation', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-feature-extraction', 'image-segmentation', 'image-text-to-text', 'image-to-image', 'keypoint-matching', 'mask-generation', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'table-question-answering', 'text-classification', 'text-generation', 'text-to-audio', 'text-to-speech', 'token-classification', 'video-classification', 'visual-question-answering', 'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection', 'translation_XX_to_YY']\""]}]},{"cell_type":"code","source":["import gradio as gr\n","from transformers import pipeline\n","from PIL import Image\n","\n","captioner = pipeline(\"image-to-text\")\n","generator = pipeline(\"text-generation\")\n","\n","def echo(message, history) :\n","\n","    # message {'text': 'text', 'files': []}\n","    # message {'text': '', 'files': ['/private/var/folders/_x/jfhn2s8d6t512jvv5lndzw2m0000gn/T/gradio/f299e1f235318c3c17f47efef61fabb3f52e14158f3c4e1a2f15b1cf0fb8329d/Gemini_Generated_Image_e9k4qje9k4qje9k4.png']}\n","\n","    # History[]\n","    # History[{'role': 'user', 'metadata': None, 'content': [{'text': 'text', 'type': 'text'}], 'options': None}, {'role': 'assistant', ']\n","    print(\"message\", message)\n","    print(\"history\", history)\n","\n","    # message에서 텍스트와 이미지 분리\n","    text = message[\"text\"]\n","\n","    if message.get(\"files\") :\n","        image = message[\"files\"][0]\n","        if image :\n","            # 이미지만\n","            result = captioner(image)\n","            return result[0]['generated_text']\n","    elif text :\n","\n","        # caption_result 값이 있다면 적절한 문장 생성하도록 만들기\n","        generator(\"In this course, we will teach you how to\")\n","\n","        return text\n","\n","\n","\n","demo = gr.ChatInterface(\n","    fn=echo,\n","    multimodal=True,\n","    title=\"멀티 모달 AI 챗봇\",\n","    description=\"이미지를 업로드하면 이미지에 대한 설명을 생성하는 챗봇입니다. 테스트로 질문도 가능합니다.\",\n",")\n","demo.launch()"],"metadata":{"id":"t_29rw9Y9I0F"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/colab/Untitled2.ipynb b/colab/Untitled2.ipynb
new file mode 100644
index 0000000..294fdc3
--- /dev/null
+++ b/colab/Untitled2.ipynb
@@ -0,0 +1 @@
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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","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"John\",\n          \"Nate\",\n          \"Jenny\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 25,\n        \"max\": 30,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          30,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"student\",\n          \"teacher\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":38}]},{"cell_type":"code","source":["df['A']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":178},"id":"5sgbHuimIHIE","executionInfo":{"status":"ok","timestamp":1779326560733,"user_tz":-540,"elapsed":7,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"115eafcb-8362-463f-abae-3f554001f792"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    1\n","1    2\n","2    3\n","Name: A, dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>A</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":42}]},{"cell_type":"code","source":["print(df.dtypes)\n","\n","df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WHf33YT8Dvwc","executionInfo":{"status":"ok","timestamp":1779326605711,"user_tz":-540,"elapsed":18,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"a51e5650-7e75-448a-ffe8-b877028890b6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["A    int64\n","dtype: object\n","<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 3 entries, 0 to 2\n","Data columns (total 1 columns):\n"," #   Column  Non-Null Count  Dtype\n","---  ------  --------------  -----\n"," 0   A       3 non-null      int64\n","dtypes: int64(1)\n","memory usage: 156.0 bytes\n"]}]},{"cell_type":"code","source":["friend_dict_list = [\n","    {\n","        \"name\" : \"John\",\n","        \"age\" : 25,\n","        \"job\" : \"student\"\n","    },\n","    {\n","        \"name\" : \"Nate\",\n","        \"age\" : 30,\n","        \"job\" : \"teacher\"\n","    },\n","    {\n","        \"name\" : \"Jenny\",\n","        \"age\" : 30,\n","        \"job\" : \"developer\"\n","    },\n","]\n","\n","df = pd.DataFrame(friend_dict_list)\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"EmoE5g5EDx9Z","executionInfo":{"status":"ok","timestamp":1779325589379,"user_tz":-540,"elapsed":13,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"730f2773-608d-4632-8645-6509cda8564c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    name  age        job\n","0   John   25    student\n","1   Nate   30    teacher\n","2  Jenny   30  developer"],"text/html":["\n","  <div id=\"df-848163e5-61bb-43c5-9a7f-d37beda61fe8\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>age</th>\n","      <th>job</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>John</td>\n","      <td>25</td>\n","      <td>student</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Nate</td>\n","      <td>30</td>\n","      <td>teacher</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Jenny</td>\n","      <td>30</td>\n","      <td>developer</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-848163e5-61bb-43c5-9a7f-d37beda61fe8')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-848163e5-61bb-43c5-9a7f-d37beda61fe8 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"job\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"student\",\n          \"teacher\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["# job\n","\n","df['job']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":178},"id":"QEo30ICuEdbc","executionInfo":{"status":"ok","timestamp":1779325632138,"user_tz":-540,"elapsed":43,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"1d1643b0-1fa4-4e72-90d5-868d518f71e2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0      student\n","1      teacher\n","2    developer\n","Name: job, dtype: object"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>job</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>student</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>teacher</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>developer</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> object</label>"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["# 코랩에서 개인 구글 드라이브를 /content/gdrive 이름으로 연결\n","\n","from google.colab import drive\n","\n","drive.mount(\"/content/gdrive\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LVbNvrpWFU1U","executionInfo":{"status":"ok","timestamp":1779325876303,"user_tz":-540,"elapsed":48542,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"59546b82-845a-4044-f8f7-ae1aac5e6d74"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["df.to_csv('/content/gdrive/MyDrive/Colab Notebooks/friends.csv', index = False, encoding=\"utf-8-sig\")"],"metadata":{"id":"swYD3GkRFhdA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# csv 파일을 데이터프레임으로 변환\n","\n","friend_df = pd.read_csv('/content/gdrive/MyDrive/Colab Notebooks/friends.csv')\n","friend_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"kYdddA21HDhY","executionInfo":{"status":"ok","timestamp":1779327276975,"user_tz":-540,"elapsed":27,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"de397b7b-a102-455a-fbf3-9fedbcf41095"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    name  age        job\n","0   John   25    student\n","1   Nate   30    teacher\n","2  Jenny   30  developer"],"text/html":["\n","  <div id=\"df-164fdcf2-d5d9-4723-a1c7-cacb4ac78ea1\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>age</th>\n","      <th>job</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>John</td>\n","      <td>25</td>\n","      <td>student</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Nate</td>\n","      <td>30</td>\n","      <td>teacher</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Jenny</td>\n","      <td>30</td>\n","      <td>developer</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-164fdcf2-d5d9-4723-a1c7-cacb4ac78ea1')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path 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30]"],"metadata":{"id":"4iLtTY7yGmLC","colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"status":"ok","timestamp":1779327375845,"user_tz":-540,"elapsed":14,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"c26068e5-89d4-48dd-8b4a-01b711b548ee"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    name  age        job\n","1   Nate   30    teacher\n","2  Jenny   30  developer"],"text/html":["\n","  <div id=\"df-cbe0a4bd-248c-444f-a741-41e8c1df96a9\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      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sns.load_dataset(\"mpg\")\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":230},"id":"M4Zw-viiL049","executionInfo":{"status":"ok","timestamp":1779327589038,"user_tz":-540,"elapsed":35,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"f9688742-b194-45ea-edc3-bfcfd4d99458"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n","0  18.0          8         307.0       130.0    3504          12.0   \n","1  15.0          8         350.0       165.0    3693          11.5   \n","2  18.0          8         318.0       150.0    3436          11.0   \n","3  16.0          8         304.0       150.0    3433          12.0   \n","4  17.0          8         302.0       140.0    3449          10.5   \n","\n","   model_year origin                       name  \n","0          70    usa  chevrolet chevelle malibu  \n","1          70    usa          buick skylark 320  \n","2          70    usa         plymouth satellite  \n","3          70    usa              amc rebel sst  \n","4          70    usa                ford torino  "],"text/html":["\n","  <div id=\"df-920b51c5-d437-41e8-9ea4-3f246cca71c3\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>mpg</th>\n","      <th>cylinders</th>\n","      <th>displacement</th>\n","      <th>horsepower</th>\n","      <th>weight</th>\n","      <th>acceleration</th>\n","      <th>model_year</th>\n","      <th>origin</th>\n","      <th>name</th>\n","    </tr>\n","  </thead>\n","  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\"std\": 38.49115993282855,\n        \"min\": 46.0,\n        \"max\": 230.0,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          92.0,\n          100.0,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 846,\n        \"min\": 1613,\n        \"max\": 5140,\n        \"num_unique_values\": 351,\n        \"samples\": [\n          3730,\n          1995,\n          2215\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"acceleration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7576889298126757,\n        \"min\": 8.0,\n        \"max\": 24.8,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          14.7,\n          18.0,\n          14.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 70,\n        \"max\": 82,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          81,\n          79,\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"origin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"usa\",\n          \"japan\",\n          \"europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 305,\n        \"samples\": [\n          \"mazda rx-4\",\n          \"ford f108\",\n          \"buick century luxus (sw)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":57}]},{"cell_type":"code","source":["# sql group by\n","df.columns\n","df.groupby(by=\"origin\")\n","df.groupby(by=\"origin\").size()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"ZaQYEL3KMFn2","executionInfo":{"status":"ok","timestamp":1779327752064,"user_tz":-540,"elapsed":19,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"4f1bb8ac-51d5-4bc1-f00d-15d4b8d4c2b9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["origin\n","europe     70\n","japan      79\n","usa       249\n","dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","    <tr>\n","      <th>origin</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>europe</th>\n","      <td>70</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>79</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>249</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["df['origin'].value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"uhx4o7KpMtbY","executionInfo":{"status":"ok","timestamp":1779327803368,"user_tz":-540,"elapsed":41,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"d52d2950-6718-44ae-d36d-4e0e6896373e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["origin\n","usa       249\n","japan      79\n","europe     70\n","Name: count, dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>count</th>\n","    </tr>\n","    <tr>\n","      <th>origin</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>usa</th>\n","      <td>249</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>79</td>\n","    </tr>\n","    <tr>\n","      <th>europe</th>\n","      <td>70</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":65}]},{"cell_type":"code","source":["df.groupby(by=\"origin\")['cylinders'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"sXhyzY1tM583","executionInfo":{"status":"ok","timestamp":1779327915265,"user_tz":-540,"elapsed":43,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"2913283e-c7b7-4008-f0ad-48c26cf514cb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["origin\n","europe    4.157143\n","japan     4.101266\n","usa       6.248996\n","Name: cylinders, dtype: float64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>cylinders</th>\n","    </tr>\n","    <tr>\n","      <th>origin</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>europe</th>\n","      <td>4.157143</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.101266</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.248996</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> float64</label>"]},"metadata":{},"execution_count":68}]},{"cell_type":"code","source":["df.groupby(by=[\"model_year\", \"origin\"])['cylinders'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"Qw3TNbsxNVRC","executionInfo":{"status":"ok","timestamp":1779327969341,"user_tz":-540,"elapsed":48,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"fc7ecde8-8c9f-433e-82e1-dbd3e09641b8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["model_year  origin\n","70          europe    4.000000\n","            japan     4.000000\n","            usa       7.636364\n","71          europe    4.000000\n","            japan     4.000000\n","            usa       6.200000\n","72          europe    4.000000\n","            japan     3.800000\n","            usa       6.888889\n","73          europe    4.000000\n","            japan     4.250000\n","            usa       7.241379\n","74          europe    4.000000\n","            japan     4.000000\n","            usa       6.266667\n","75          europe    4.000000\n","            japan     4.000000\n","            usa       6.400000\n","76          europe    4.250000\n","            japan     4.500000\n","            usa       6.363636\n","77          europe    4.000000\n","            japan     4.166667\n","            usa       6.222222\n","78          europe    4.833333\n","            japan     4.000000\n","            usa       6.000000\n","79          europe    4.250000\n","            japan     4.000000\n","            usa       6.260870\n","80          europe    4.111111\n","            japan     4.076923\n","            usa       4.285714\n","81          europe    4.500000\n","            japan     4.333333\n","            usa       4.923077\n","82          europe    4.000000\n","            japan     4.000000\n","            usa       4.300000\n","Name: cylinders, dtype: float64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th></th>\n","      <th>cylinders</th>\n","    </tr>\n","    <tr>\n","      <th>model_year</th>\n","      <th>origin</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">70</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>7.636364</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">71</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.200000</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">72</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>3.800000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.888889</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">73</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.250000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>7.241379</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">74</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.266667</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">75</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.400000</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">76</th>\n","      <th>europe</th>\n","      <td>4.250000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.500000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.363636</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">77</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.166667</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.222222</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">78</th>\n","      <th>europe</th>\n","      <td>4.833333</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.000000</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">79</th>\n","      <th>europe</th>\n","      <td>4.250000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>6.260870</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">80</th>\n","      <th>europe</th>\n","      <td>4.111111</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.076923</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>4.285714</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">81</th>\n","      <th>europe</th>\n","      <td>4.500000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.333333</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>4.923077</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"3\" valign=\"top\">82</th>\n","      <th>europe</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>japan</th>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>usa</th>\n","      <td>4.300000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> float64</label>"]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":["df = pd.DataFrame({\"yyyy-mm-dd\" : [\"2005-09-28\", \"2007-10-05\", \"2013-12-20\"]})\n","df\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"q5fnn_YIOQDP","executionInfo":{"status":"ok","timestamp":1779328236481,"user_tz":-540,"elapsed":46,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"bafd8d11-5b24-4a39-f121-1d2ab1368aa2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   yyyy-mm-dd\n","0  2005-09-28\n","1  2007-10-05\n","2  2013-12-20"],"text/html":["\n","  <div id=\"df-ef4bc30b-38ad-446f-b9f3-8147ee57c34d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>yyyy-mm-dd</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2005-09-28</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2007-10-05</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2013-12-20</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div 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\"2005-12-20\"\n","\n","data[0:4]\n","\n","data.split(\"-\")\n","data.split(\"-\")[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"39M80FJ2PXhI","executionInfo":{"status":"ok","timestamp":1779328505089,"user_tz":-540,"elapsed":33,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"113c9d01-75e7-40d6-845e-ae267c222b37"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2005'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":89}]},{"cell_type":"code","source":["#  년도 컬럼 추가\n","\n","# 새로운 컬럽 추가\n","# df['year'] = [2005, 2007, 2012]\n","\n","def extract_year(column):\n","  return data.split(\"-\")[0]"],"metadata":{"id":"UnFGi827OYDY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# df에 extract_year() 함수 적용\n","# lamda 함수도 가능\n","\n","df['year'] = df['yyyy-mm-dd'].apply(extract_year)\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"F2mDvKynP2oA","executionInfo":{"status":"ok","timestamp":1779328889246,"user_tz":-540,"elapsed":47,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"bfe6b609-85dd-495b-e8ee-95724fd32f0d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   yyyy-mm-dd  year\n","0  2005-09-28  2005\n","1  2007-10-05  2005\n","2  2013-12-20  2005"],"text/html":["\n","  <div id=\"df-4422e719-e466-465f-bfd4-46e39e379f9e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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]\n}"}},"metadata":{},"execution_count":99}]},{"cell_type":"code","source":["!pip install koreanize_matplotlib"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xKctvdbWTXaE","executionInfo":{"status":"ok","timestamp":1779329553031,"user_tz":-540,"elapsed":10407,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"f8711db5-6887-46de-89f6-02c0cd3228f9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting koreanize_matplotlib\n","  Downloading koreanize_matplotlib-0.1.1-py3-none-any.whl.metadata (992 bytes)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (from koreanize_matplotlib) (3.10.0)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (1.3.3)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (4.62.1)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (1.5.0)\n","Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (26.1)\n","Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (11.3.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (3.3.2)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib->koreanize_matplotlib) (2.9.0.post0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib->koreanize_matplotlib) (1.17.0)\n","Downloading koreanize_matplotlib-0.1.1-py3-none-any.whl (7.9 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m67.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: koreanize_matplotlib\n","Successfully installed koreanize_matplotlib-0.1.1\n"]}]},{"cell_type":"code","source":["# matplotlib (시각화 라이브러리 기본)\n","\n","import matplotlib.pyplot as plt\n","import koreanize_matplotlib"],"metadata":{"id":"eUo14MAQRO__"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot([10,20,30,40])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"Ki9qYiTwRDDT","executionInfo":{"status":"ok","timestamp":1779329080312,"user_tz":-540,"elapsed":252,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"716f3a0f-14f0-4213-90df-90b8a884b89b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7c252c06e7e0>]"]},"metadata":{},"execution_count":102},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.plot([1,2,3,4], [12,43,25,15])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"Qc3zzgEPRywY","executionInfo":{"status":"ok","timestamp":1779329226423,"user_tz":-540,"elapsed":294,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"3cf090a7-e44c-4c86-ce95-4b483c497e2e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7c2528c0d490>]"]},"metadata":{},"execution_count":108},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["data_a = {\"data_x\" : [1,2,3,4,5,], \"data_y\" : [2,3,5,10,8]}\n","df = pd.DataFrame(data_a)\n","df\n","\n","plt.plot(df['data_x'], df['data_y'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"rmeLIvsVSVT7","executionInfo":{"status":"ok","timestamp":1779329336058,"user_tz":-540,"elapsed":414,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"3ed97644-e4bf-4553-dd6b-e1e4e2f1c409"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7c2528c0f830>]"]},"metadata":{},"execution_count":112},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.bar(df['data_x'], df['data_y'])\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"J-qeo_ozS4xt","executionInfo":{"status":"ok","timestamp":1779329453563,"user_tz":-540,"elapsed":460,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"1bb014de-f99b-4dd5-b74b-c20963c5269b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BarContainer object of 5 artists>"]},"metadata":{},"execution_count":116},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAFOVJREFUeJzt3X+s1XX9wPHXFeaF7HIVAvQOECqTBDF/xQhzmqRjyOCflo6KqGmr6w9ildwtMKZ4sTVHJQNiS2gT0bWBTSfMsSFzifIjm7SFUJi3FOjnvXCbN8c93z9ad13BX98+5/W5597HY/v8cT7n3Xm/9rGN5z7nnHvqKpVKJQAAkpxR9gAAwMAiPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVIPLHuCturu747XXXouGhoaoq6srexwA4D2oVCpx/PjxaGpqijPOeOd7G30uPl577bUYO3Zs2WMAAP8PbW1tMWbMmHdc0+fio6GhISL+PfywYcNKngYAeC86Ojpi7NixPf+Ov5M+Fx//eatl2LBh4gMAasx7+ciED5wCAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQ6n3Hx86dO2P27NnR1NQUdXV1sWXLll7PVyqVWLp0aZx33nkxdOjQmDFjRhw8eLCoeQGAGve+46OzszMuueSSWLVq1Wmf//73vx8/+tGPYs2aNfH888/HWWedFTfccEO88cYb//OwAEDte98/LDdz5syYOXPmaZ+rVCqxcuXK+O53vxtz5syJiIif/exnMXr06NiyZUvcdNNN/9u0AEDNK/QzH4cPH44jR47EjBkzes41NjbG1KlT47nnnjvt/6arqys6Ojp6HQBA//W+73y8kyNHjkRExOjRo3udHz16dM9zb9Xa2hrLli0rcgyAUo1f/GTZI9SMV1bMKnsESlD6t11aWlqivb2952hrayt7JACgigqNj3PPPTciIo4ePdrr/NGjR3uee6v6+voYNmxYrwMA6L8KjY8JEybEueeeG9u3b+8519HREc8//3xMmzatyK0AgBr1vj/zceLEiTh06FDP48OHD8eLL74Yw4cPj3HjxsXChQvj3nvvjQsuuCAmTJgQS5Ysiaamppg7d26RcwMANep9x8eePXvi2muv7Xm8aNGiiIiYP39+rF+/Pr7zne9EZ2dn3HrrrfGPf/wjrrrqqti6dWsMGTKkuKkBgJpVV6lUKmUP8d86OjqisbEx2tvbff4DqEm+7fLe+bZL//F+/v0u/dsuAMDAIj4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFSFx8fJkydjyZIlMWHChBg6dGh85CMfiXvuuScqlUrRWwEANWhw0S94//33x+rVq2PDhg0xadKk2LNnTyxYsCAaGxvjjjvuKHo7AKDGFB4fv/zlL2POnDkxa9asiIgYP358PPLII/HCCy8UvRUAUIMKf9vlU5/6VGzfvj1efvnliIj49a9/Hc8++2zMnDnztOu7urqio6Oj1wEA9F+F3/lYvHhxdHR0xMSJE2PQoEFx8uTJWL58ecybN++061tbW2PZsmVFjwHAADN+8ZNlj1AzXlkxq9T9C7/z8dhjj8XDDz8cGzdujH379sWGDRviBz/4QWzYsOG061taWqK9vb3naGtrK3okAKAPKfzOx7e//e1YvHhx3HTTTRERcfHFF8cf/vCHaG1tjfnz55+yvr6+Purr64seAwDoowq/8/HPf/4zzjij98sOGjQouru7i94KAKhBhd/5mD17dixfvjzGjRsXkyZNil/96lfxwAMPxFe+8pWitwIAalDh8fHjH/84lixZEt/4xjfi2LFj0dTUFF/72tdi6dKlRW8FANSgwuOjoaEhVq5cGStXriz6pQGAfsBvuwAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqaoSH3/605/iC1/4QowYMSKGDh0aF198cezZs6caWwEANWZw0S/497//PaZPnx7XXnttPPXUUzFy5Mg4ePBgnHPOOUVvBQDUoMLj4/7774+xY8fGQw891HNuwoQJRW8DANSowt92+cUvfhFXXHFFfO5zn4tRo0bFpZdeGuvWrXvb9V1dXdHR0dHrAAD6r8Lj4/e//32sXr06Lrjggti2bVt8/etfjzvuuCM2bNhw2vWtra3R2NjYc4wdO7bokQCAPqTw+Oju7o7LLrss7rvvvrj00kvj1ltvjVtuuSXWrFlz2vUtLS3R3t7ec7S1tRU9EgDQhxQeH+edd15cdNFFvc59/OMfj1dfffW06+vr62PYsGG9DgCg/yo8PqZPnx4HDhzode7ll1+O888/v+itAIAaVHh8fPOb34xdu3bFfffdF4cOHYqNGzfGT37yk2hubi56KwCgBhUeH1deeWVs3rw5HnnkkZg8eXLcc889sXLlypg3b17RWwEANajwv/MREXHjjTfGjTfeWI2XBgBqnN92AQBSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AINXgsgcAqmf84ifLHqFmvLJiVtkjwIDhzgcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpqh4fK1asiLq6uli4cGG1twIAakBV42P37t2xdu3amDJlSjW3AQBqSNXi48SJEzFv3rxYt25dnHPOOdXaBgCoMVWLj+bm5pg1a1bMmDHjHdd1dXVFR0dHrwMA6L8GV+NFN23aFPv27Yvdu3e/69rW1tZYtmxZNcYAAPqgwu98tLW1xZ133hkPP/xwDBky5F3Xt7S0RHt7e8/R1tZW9EgAQB9S+J2PvXv3xrFjx+Kyyy7rOXfy5MnYuXNnPPjgg9HV1RWDBg3qea6+vj7q6+uLHgMA6KMKj4/rrrsuXnrppV7nFixYEBMnToy77rqrV3gAAANP4fHR0NAQkydP7nXurLPOihEjRpxyHgAYePyFUwAgVVW+7fJWO3bsyNgGAKgB7nwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKkGlz0AA8P4xU+WPULNeGXFrLJHAKgqdz4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIVXh8tLa2xpVXXhkNDQ0xatSomDt3bhw4cKDobQCAGlV4fDzzzDPR3Nwcu3btiqeffjrefPPNuP7666Ozs7PorQCAGjS46BfcunVrr8fr16+PUaNGxd69e+Pqq68uejsAoMYUHh9v1d7eHhERw4cPP+3zXV1d0dXV1fO4o6Oj2iMBACWqanx0d3fHwoULY/r06TF58uTTrmltbY1ly5ZVc4xexi9+Mm2vWvfKillljwBAP1TVb7s0NzfH/v37Y9OmTW+7pqWlJdrb23uOtra2ao4EAJSsanc+brvttnjiiSdi586dMWbMmLddV19fH/X19dUaAwDoYwqPj0qlErfffnts3rw5duzYERMmTCh6CwCghhUeH83NzbFx48Z4/PHHo6GhIY4cORIREY2NjTF06NCitwMAakzhn/lYvXp1tLe3xzXXXBPnnXdez/Hoo48WvRUAUIOq8rYLAMDb8dsuAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAECqqsXHqlWrYvz48TFkyJCYOnVqvPDCC9XaCgCoIVWJj0cffTQWLVoUd999d+zbty8uueSSuOGGG+LYsWPV2A4AqCFViY8HHnggbrnllliwYEFcdNFFsWbNmvjABz4QP/3pT6uxHQBQQwYX/YL/+te/Yu/evdHS0tJz7owzzogZM2bEc889d8r6rq6u6Orq6nnc3t4eEREdHR1FjxYREd1d/6zK6/ZHRf43cN3fO9e9HK57OVz3clTj39j/vGalUnnXtYXHx1/+8pc4efJkjB49utf50aNHx29/+9tT1re2tsayZctOOT927NiiR+N9alxZ9gQDk+teDte9HK57Oap53Y8fPx6NjY3vuKbw+Hi/WlpaYtGiRT2Pu7u7429/+1uMGDEi6urqSpwsR0dHR4wdOzba2tpi2LBhZY8zYLju5XDdy+G6l2OgXfdKpRLHjx+Ppqamd11beHx86EMfikGDBsXRo0d7nT969Gice+65p6yvr6+P+vr6XufOPvvsosfq84YNGzYg/s/Z17ju5XDdy+G6l2MgXfd3u+PxH4V/4PTMM8+Myy+/PLZv395zrru7O7Zv3x7Tpk0rejsAoMZU5W2XRYsWxfz58+OKK66IT37yk7Fy5cro7OyMBQsWVGM7AKCGVCU+Pv/5z8ef//znWLp0aRw5ciQ+8YlPxNatW0/5ECr/ftvp7rvvPuWtJ6rLdS+H614O170crvvbq6u8l+/EAAAUxG+7AACpxAcAkEp8AACpxAcAkEp8lGjnzp0xe/bsaGpqirq6utiyZUvZI/V7ra2tceWVV0ZDQ0OMGjUq5s6dGwcOHCh7rH5v9erVMWXKlJ4/tjRt2rR46qmnyh5rwFmxYkXU1dXFwoULyx6lX/ve974XdXV1vY6JEyeWPVafIj5K1NnZGZdcckmsWrWq7FEGjGeeeSaam5tj165d8fTTT8ebb74Z119/fXR2dpY9Wr82ZsyYWLFiRezduzf27NkTn/nMZ2LOnDnxm9/8puzRBozdu3fH2rVrY8qUKWWPMiBMmjQpXn/99Z7j2WefLXukPqX033YZyGbOnBkzZ84se4wBZevWrb0er1+/PkaNGhV79+6Nq6++uqSp+r/Zs2f3erx8+fJYvXp17Nq1KyZNmlTSVAPHiRMnYt68ebFu3bq49957yx5nQBg8ePBpf1KEf3PngwGtvb09IiKGDx9e8iQDx8mTJ2PTpk3R2dnpJxeSNDc3x6xZs2LGjBlljzJgHDx4MJqamuLDH/5wzJs3L1599dWyR+pT3PlgwOru7o6FCxfG9OnTY/LkyWWP0++99NJLMW3atHjjjTfigx/8YGzevDkuuuiissfq9zZt2hT79u2L3bt3lz3KgDF16tRYv359XHjhhfH666/HsmXL4tOf/nTs378/Ghoayh6vTxAfDFjNzc2xf/9+78UmufDCC+PFF1+M9vb2+PnPfx7z58+PZ555RoBUUVtbW9x5553x9NNPx5AhQ8oeZ8D477fTp0yZElOnTo3zzz8/HnvssfjqV79a4mR9h/hgQLrtttviiSeeiJ07d8aYMWPKHmdAOPPMM+OjH/1oRERcfvnlsXv37vjhD38Ya9euLXmy/mvv3r1x7NixuOyyy3rOnTx5Mnbu3BkPPvhgdHV1xaBBg0qccGA4++yz42Mf+1gcOnSo7FH6DPHBgFKpVOL222+PzZs3x44dO2LChAlljzRgdXd3R1dXV9lj9GvXXXddvPTSS73OLViwICZOnBh33XWX8Ehy4sSJ+N3vfhdf/OIXyx6lzxAfJTpx4kSvEj58+HC8+OKLMXz48Bg3blyJk/Vfzc3NsXHjxnj88cejoaEhjhw5EhERjY2NMXTo0JKn679aWlpi5syZMW7cuDh+/Hhs3LgxduzYEdu2bSt7tH6toaHhlM8znXXWWTFixAifc6qib33rWzF79uw4//zz47XXXou77747Bg0aFDfffHPZo/UZ4qNEe/bsiWuvvbbn8aJFiyIiYv78+bF+/fqSpurfVq9eHRER11xzTa/zDz30UHz5y1/OH2iAOHbsWHzpS1+K119/PRobG2PKlCmxbdu2+OxnP1v2aFC4P/7xj3HzzTfHX//61xg5cmRcddVVsWvXrhg5cmTZo/UZdZVKpVL2EADAwOHvfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJDq/wAfLXh/ayhUeAAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.bar(['강백호', '서태웅', '정대만'], [175, 187, 184])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"yP0EhomPTmTN","executionInfo":{"status":"ok","timestamp":1779329646399,"user_tz":-540,"elapsed":169,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"97ba4010-9eac-4c60-add7-52261e25515e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BarContainer object of 3 artists>"]},"metadata":{},"execution_count":124},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.barh(['강백호', '서태웅', '정대만'], [175, 187, 184])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"0JyNk-W-T74G","executionInfo":{"status":"ok","timestamp":1779329679868,"user_tz":-540,"elapsed":140,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"022cbf17-75ba-4025-aaa7-e7352a83044e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BarContainer object of 3 artists>"]},"metadata":{},"execution_count":125},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["values = [175, 187, 184]\n","labels = [\"python\", \"java\", \"c#\"]\n","\n","plt.pie(values, labels=labels, autopct=\"%.1f%%\", startangle=90, counterclock=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":571},"id":"wDYAHv1uUE9H","executionInfo":{"status":"ok","timestamp":1779330401490,"user_tz":-540,"elapsed":553,"user":{"displayName":"쿠니","userId":"18312388301484735023"}},"outputId":"c91f8a37-01ad-4d70-9a6c-b6d63f411558"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["([<matplotlib.patches.Wedge at 0x7c252b6fe660>,\n","  <matplotlib.patches.Wedge at 0x7c252b6fd370>,\n","  <matplotlib.patches.Wedge at 0x7c25289cc0b0>],\n"," [Text(0.9297090875686453, 0.5879124190662052, 'python'),\n","  Text(0.05693733236902952, -1.0985254390239212, 'java'),\n","  Text(-0.9588938707429671, 0.5390014328845241, 'c#')],\n"," [Text(0.5071140477647156, 0.32067950130883915, '32.1%'),\n","  Text(0.031056726746743372, -0.5991956940130478, '34.2%'),\n","  Text(-0.5230330204052547, 0.2940007815733767, '33.7%')])"]},"metadata":{},"execution_count":161},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]}]}
\ No newline at end of file
diff --git a/colab/cat.jfif b/colab/cat.jfif
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diff --git a/colab/friends.csv b/colab/friends.csv
new file mode 100644
index 0000000..559bcf9
--- /dev/null
+++ b/colab/friends.csv
@@ -0,0 +1,4 @@
+name,age,job
+John,25,student
+Nate,30,teacher
+Jenny,30,developer
diff --git a/colab/obama.mp3 b/colab/obama.mp3
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diff --git a/colab/seoul.png b/colab/seoul.png
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diff --git a/colab/voice.mp3 b/colab/voice.mp3
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diff --git a/colab/트랜스포머로 무엇을 할 수 있나요.ipynb b/colab/트랜스포머로 무엇을 할 수 있나요.ipynb
new file mode 100755
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--- /dev/null
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+{"cells":[{"cell_type":"markdown","metadata":{"id":"uSL8FkrzEQH-"},"source":["# 트랜스포머로 무엇을 할 수 있나요?"]},{"cell_type":"markdown","metadata":{"id":"75RipA00EQH_"},"source":["Install the Transformers, Datasets, and Evaluate libraries to run this notebook."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bdIxnISxEQIA","colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779242770789,"user_tz":-540,"elapsed":16770,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"28f2d47b-1acd-4d1d-813a-701fc8720c8a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Collecting evaluate\n","  Downloading evaluate-0.4.6-py3-none-any.whl.metadata (9.5 kB)\n","Collecting transformers==4.41.2 (from transformers[sentencepiece]==4.41.2)\n","  Downloading transformers-4.41.2-py3-none-any.whl.metadata (43 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tokenizers-0.19.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m132.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: huggingface-hub, tokenizers, transformers, evaluate\n","  Attempting uninstall: huggingface-hub\n","    Found existing installation: huggingface_hub 1.11.0\n","    Uninstalling huggingface_hub-1.11.0:\n","      Successfully uninstalled huggingface_hub-1.11.0\n","  Attempting uninstall: tokenizers\n","    Found existing installation: tokenizers 0.22.2\n","    Uninstalling tokenizers-0.22.2:\n","      Successfully uninstalled tokenizers-0.22.2\n","  Attempting uninstall: transformers\n","    Found existing installation: transformers 5.0.0\n","    Uninstalling transformers-5.0.0:\n","      Successfully uninstalled transformers-5.0.0\n","Successfully installed evaluate-0.4.6 huggingface-hub-0.36.2 tokenizers-0.19.1 transformers-4.41.2\n"]}],"source":["# summarization task 때문에 transformers[sentencepiece]==4.41.2 으로 설치\n","!pip install datasets evaluate transformers[sentencepiece]==4.41.2"]},{"cell_type":"markdown","source":["### pipeline(task, model)\n","- 복잡한 모델 과정을 간단하게 묶은 기능"],"metadata":{"id":"Aojrijn_Jwk3"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"NHq4d-UAEQIA","outputId":"e695fe69-6e23-4503-bb05-f91b8b2cb436","colab":{"base_uri":"https://localhost:8080/","height":367,"referenced_widgets":["7d13d4ce88694b8584ec5e1632d316ab","7ed0a2772ad84bc18bb83f5501888b49","b8829817cd414c3f829eefd7890788d7","d5ddbf9ee02e4c15b5d09358d054ff32","ad942adc227346ec914f78d560bb3edf","769183765e654925ac4dc5c53c1f75a9","0d80260998c1463ba2184ef65f26e353","6d7e57e523714c3e985e64aee31159df","519c6d50ba3742f181fc63a68e551841","eef0a8930bd14357aa5e0911d062d748","241c0299e8f7482389869952935b396c","a8ffed7bd17c4188bc4b72cfdeb9c180","fcca168636654d52b97d1fb8e72d4aa3","2022522dc0f644279e15a3d8cbd7c042","c4b2ab5b2bb845f19dd19882e3685df0","11d572fda153468991d99f90cbc2af1d","63fadac2565745b8a63cf9a18ee66ae1","748950d3a0c3445eb7758b8109358de3","6387d722c1c44aa9bd8be0a769d8d7aa","744f53ba86324ce5a977cebb47ab51e4","c0b3450c998d46c89e605e2b97d4d444","53b80097ca5e4b979f48ab62251cfe0b","0a6abb5cea5b4d40b50aa1b64fcb47bd","2c8bff41da2e44cdb8b6b9b1b6be675e","0a460f12673b453b884bc50cb5ae434d","84d3be7c19c1460591caca5bbff2e4b0","d4b069d065ef47dab048d81ee0624667","b18d919425e2421bb069384153081aab","4cf2274fde93424db5eb3bd9199dc1bb","bd91545514f244d192ab99cd760eeca5","948fdd020c5e47389f6158008fb276a3","0f9b87a6416b47168a28a320850fc51e","eb10f2ec86f342db804180ce0362f232","b644e4ac94304ed3bf92183b878c3be1","4c94b8d7494d4ce1b3e30fe196472cd4","3a4806ab2a0b44c4bfa6e32d111f5681","a2dc4266558a4428ab714d07d1983ff0","3f743fdb83a243799153d7d815557240","a082982d38954aeeb10b9a659e10b86b","58046ec4b61a4b3e8586d8453511de22","c5dbdc9af4ba4a029febf5f51d29675b","188bf4b569b74bd6aaf88f275198d9c3","ea6c675820cd4a508d4fbb85ab16f179","362d4042c57245bf9480683b8c16eb0c"]},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779159338382,"user_tz":-540,"elapsed":23167,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7d13d4ce88694b8584ec5e1632d316ab"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a8ffed7bd17c4188bc4b72cfdeb9c180"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0a6abb5cea5b4d40b50aa1b64fcb47bd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b644e4ac94304ed3bf92183b878c3be1"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["[{'label': 'POSITIVE', 'score': 0.9598049521446228}]"]},"metadata":{},"execution_count":2}],"source":["from transformers import pipeline\n","\n","# task 지정, 모델지정\n","# 기본모델 : distilbert/distilbert-base-uncased-finetuned-sst-2-english\n","classifier = pipeline(\"sentiment-analysis\")\n","classifier(\"I've been waiting for a HuggingFace course my whole life.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n2dqAYuTEQIB","outputId":"93e65a09-acd0-48f9-fd33-191437da72ec","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1779159603821,"user_tz":-540,"elapsed":219,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'label': 'POSITIVE', 'score': 0.9598049521446228},\n"," {'label': 'NEGATIVE', 'score': 0.9994558691978455},\n"," {'label': 'POSITIVE', 'score': 0.7493420243263245},\n"," {'label': 'POSITIVE', 'score': 0.6790701150894165},\n"," {'label': 'POSITIVE', 'score': 0.7637550830841064}]"]},"metadata":{},"execution_count":3}],"source":["classifier(\n","    [\"I've been waiting for a HuggingFace course my whole life.\",\n","     \"I hate this so much!\",\n","     \"행복합니다.\",\"즐겁다\",\"힘들다\"]\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"m2MnA7PwEQIC","outputId":"01de70b0-7ac0-435e-d793-69e2c17afdd6","colab":{"base_uri":"https://localhost:8080/","height":358,"referenced_widgets":["b5fe04a9fb69498a97cfad6faf4c0289","6757b6dc78184b23a0742666373b540a","e5fb70d212034312af9708a5fc5819d5","cba942de9c7a46c19c62bef873e6538e","69bdfc51e07a4a12bcc0d3a66b146f17","9852c5ad6c29405c927d98b66d78a5bf","f11193af50534e0c869a8b37c5e2713d","90943101d41f4fc980d8372e8c1d5174","65389434f63348d3a030854513e35bfd","6e8bed449dfb4c92b76c9011bc2969b6","75f9dedd3c80455eb3a45e3005f66bde","5d04903b75504ac98cf5a7e3d9e11e2c","20b7b73c4a9d4eb0b536374c08ae56df","a94c557da7e9489abe2c854dc0a17f11","5b966a64853442de8dc5a89d64bc941b","6d0fef2f33304b90b41962677b5473a7","f89cd21f2e82436a864b8fb14959068a","974835af58dc42bb8768df777dacf468","2c33be753d67417caf9ee1bbf5f37d56","195d219d07744caa89e4b2543494232e","12132714aea34221a8db87708dfaaefd","b5dd2d2334854f0e91726faa17c107d5","5986e2bd9773424eb7e183a53ce30600","6da21f71ee3847159eadca78588de48d","b6c2404f6720477b98a5dc49269d62db","6a5577c9b881467a82942737a3446363","452c6c46c342454090af42967dbe1188","b597dd9d3bd140fa9ea87bbf4c001414","f88e4f5db38245ceb232de398a809016","7564dcd8012741369d1e2199720fc33e","41ae38817db94cfd93d8f89471d42c08","28f6e34b6a8c41c2a11926aa4ae82708","2e1feb7d19e24a9baa8cc0cfb70dd6cf","5b9af5d9b40d42608673797685d23777","dfded9d8f71f453e9424bb3e14b59553","45b168171a9843f6b9cba64af2d9bca3","da424ef2861c4bc1afa2b36cabd87cc1","ad8b26d1429243549ce3dbb70f7aa078","8d1ee699320645e5b515f4b86dddd5fe","527216412ba44a22bccb96d0b8ac6638","186a8f4cd0524671af4bd5a755a9dd7f","c75e9a57227f49ec9c08863a97203c78","d827157beebd49d38cf816b1709bcedd","e46e1bc1588143b2a8bf2ecbfcc167b1","da9c907f5a014d43b12ac655f66e963a","7a73303cada342608958f199778a2338","cf99550044204dd8965bee2f7f6494bc","1c7c76d166604fad8f5c78bf3be1b65d","237f4a53480947ccb210ce7b4587cab2","b991ad9f501a4ab083012090c4769c80","f08717592c6b4e33922e582e5190ba4f","accc87199cc543fc8438dfd25e0c46da","8098a01f889c4dd29c65026934e9ad82","fc17fb52eb36417eb08f29356e1f6d21","118c5ad9419944b2a472df8e680bd89d","0735695fff8746e286242a4a3b6a0905","62eead0fc8364c81b92f2fcd89b1a8e5","e4cbc5b3b8dd4c02a8ce9f0a194e419e","95657275db3843d7abda2caacd256c4d","86d47c0edff2437d8ba82992fa0990d2","4b1ae94102b54a32b4674d3a807ac9ac","49b6520329b24ccaa755e92b0e42ee00","85d6485d6e4544c48feb73196c6421d8","c1ac07647978425ca9b2ae2c6f040817","3054b006e7b4452bbe71d356945a2829","3db51a28be3f457b839e871bb65c37b3"]},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779159769909,"user_tz":-540,"elapsed":33874,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to facebook/bart-large-mnli and revision c626438 (https://huggingface.co/facebook/bart-large-mnli).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b5fe04a9fb69498a97cfad6faf4c0289"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5d04903b75504ac98cf5a7e3d9e11e2c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5986e2bd9773424eb7e183a53ce30600"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5b9af5d9b40d42608673797685d23777"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"da9c907f5a014d43b12ac655f66e963a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0735695fff8746e286242a4a3b6a0905"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'sequence': 'This is a course about the Transformers library',\n"," 'labels': ['education', 'business', 'politics'],\n"," 'scores': [0.8445985913276672, 0.11197450757026672, 0.04342687129974365]}"]},"metadata":{},"execution_count":4}],"source":["from transformers import pipeline\n","\n","# 주제 찾기 : zero-shot-classification\n","# facebook/bart-large-mnli\n","classifier = pipeline(\"zero-shot-classification\")\n","classifier(\n","    \"This is a course about the Transformers library\",\n","    candidate_labels=[\"education\", \"politics\", \"business\"],\n",")"]},{"cell_type":"code","source":["classifier(\n","    \"one day I will see the world\",\n","    candidate_labels=[\"travel\", \"cooking\", \"dancing\"],\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MFwD68C4MN2N","executionInfo":{"status":"ok","timestamp":1779159918715,"user_tz":-540,"elapsed":1264,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"1a44a91b-135d-40db-d3bb-dad399f3e8d5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sequence': 'one day I will see the world',\n"," 'labels': ['travel', 'dancing', 'cooking'],\n"," 'scores': [0.9938650727272034, 0.0032737997826188803, 0.002861031796783209]}"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Od9zlLUiEQIC","outputId":"d74faf90-c7d1-44a6-a00f-514ab913d62a","colab":{"base_uri":"https://localhost:8080/","height":371,"referenced_widgets":["fe156b32e31240efb792106c05efb1d4","934df97fc2d642aa9eee15b702177077","11bf42b74dda434f984e6e907cf05609","e447748b08554bd292846d21311905a7","be5c2cbd737a4de3a37d684a65ad6305","3eea4e3abe9c4e6aadd413fb83c9400d","386651121cf44c7cb1b03f17df9745f0","6959720fd1af4909a9be97eae7428ec1","d7c4d751fcbd450fa7c4c7a271d5bf0e","711d62cb6da54b4487851894516c7308","d5ba5faaf1d9433cbfe90ca002f69f67","2e07b45534d9480f94622a064d3a07c3","68e8cbe50804445f815cbf1574a38f30","4c98428672714f38916243995457db31","4d3fa3da1b544aebbf232f8ae3bfbfb8","aa59193e430f4b50b1e81d6ae69037f2","78de1916f580406caf5885fe9156aec5","ffce3e036ace4f52b25492c3d8eae393","94c08ce6d61943e88c0302d4c0013af7","6fdd87dca06f4583be21bde0a9f23ee9","b49f34c5725f4adf9f9a376ba166acb5","2435d5e423a64d08a3d173d4671438d4","c43c952b83dc413caf9f04689e23d187","414a08503ad04229bf0d8596f24e1c47","69c53e11b18942e98d7aceef428dbc7d","daaa71442b57428aa7a9054840d43cb4","cb63f6a6dc164373af35397cc8f2d7bb","d6e8744ee7fd44f886895e87916be9f3","a81b3ca2fe8a4473806dc94706d9f033","4592c87d94a9472d9881480b37c94f7d","de3c3672b30f49fbaee780efd55d800a","40202672591d414fbe076a8ba4204086","86d0d5ff462a496ab833d0bd0fcfa7b1","542eb3f0adc5487093eeb6f9ae960e19","087391263685432e981deb354955b117","d32d0e3b58e0486899abac257df72b8f","8f1a0e53a7004be4a7229544aafe229e","f3af112144d541448e928ad38ecd6313","668fefe5694349ee9d9861267d9f1591","e48176ae7dae48829291609bd2e7c843","bd0dad751f0e4580b07bb81888477137","02a8da8943944f8fa65885df73f9e3fe","9937930246ff436fb46a3106bc80267c","6929c588d5f64ac093aac669ec2cd367","a79c9d37c0114777a3ad424891b0874e","b04173c233394989a1fcb8620805fbaf","99201100807648128ea74044a3f275b8","e0ffad2e794d443695127da7e875522c","848e2744cd454ff780c5bc6407b0e5fe","a8935ef709204b6dbc252c9a77ac7d65","f953c3617e9e4b3ca73b1b7058290bf9","845c685773564a3cb52c6fb062a46e84","03024153641b41e2954b9bf05c903cba","ef504cfe1aed40699580580964930585","0aa5f859676e4f45933e4d3cd347f85d","960b0336c86e45d799236b6aedeec677","32c3de527bf042cdbad12ff7035f5698","ae61e60012464469816b9e6ee0e9af74","c56df7cd5f1343f59c2cce79d04bd50e","8a287b2eec994d0093c85678e422aabc","a5c4d80a60fe4e318ea35c1836101dbd","80ddafa876c9472997d7562917eca7a8","d05b8e809f3248098f05663b4d9a8590","9c20800c62574170a0dec2004f8fad0b","5b1825306649447fa297ff5692ac6609","aecbdbc5f93242e3a718ce84275f70ee","82ce035a2adc4bb4b7b56f4b78e1521d","828b32b4d26f4804a01359172939730f","c3dffdbb68da4ea4a629f99619c94436","5c4a0fbc730d4801a344554f546f065e","1bbdfbcaedca4853929ebd17ba665f2a","8c08aec611024ce989ebf03eb7f1b32d","08f0b4a564454366845646e0049c2009","a528739e1d0149dd8c562b497e4173d7","67dba886c3a64b4994270900c79efbb3","9577570e3b6b4d15846e447af1ad3b40","9cfc75ec217c49c889931c673d19f886"]},"executionInfo":{"status":"ok","timestamp":1779160026699,"user_tz":-540,"elapsed":10906,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to openai-community/gpt2 and revision 6c0e608 (https://huggingface.co/openai-community/gpt2).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fe156b32e31240efb792106c05efb1d4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2e07b45534d9480f94622a064d3a07c3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c43c952b83dc413caf9f04689e23d187"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"542eb3f0adc5487093eeb6f9ae960e19"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a79c9d37c0114777a3ad424891b0874e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"960b0336c86e45d799236b6aedeec677"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"82ce035a2adc4bb4b7b56f4b78e1521d"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'generated_text': 'In this course, we will teach you how to build a beautiful virtual reality game that uses just your own imagination. You also must develop the ability to make a game in Virtual Reality. We recommend that you play the game in front of a computer,'}]"]},"metadata":{},"execution_count":6}],"source":["from transformers import pipeline\n","\n","# openai-community/gpt2\n","generator = pipeline(\"text-generation\")\n","generator(\"In this course, we will teach you how to\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fKS97jqREQIC","outputId":"c6f39f33-ab25-4ebe-89c5-00ee4de88937","colab":{"base_uri":"https://localhost:8080/","height":371,"referenced_widgets":["4cfee9edb86045dc8bc4f66e4c004711","606d613261ec4f5880289026efdb9cf0","a2f12546d36e42b99198ec7be91f13b5","8b96c42222144246ab6fdd42914aed0b","f3f6d1d2183247049b799c3c629a1465","9d83dae08fc640d2adefc81c56a35bcd","98a42cd837a84f4dbff2c529be3907c6","c49c20ba8b724f0a933cc80547df9734","aa1598262c2e4b7ca91b0f01a75bc885","23a242a43f5f4353bdbaea91b3521192","3e6c431dac1e4822874a9cc422644398","81832d31c786492ca65ee7ebca5592ad","611e0537a5e747f39826d168556bffb4","226c75ed72924fa9b8a5230f49aface2","2c453533ff1e425283b16d335f360752","2f9fe22d3385447da83100711ab72df1","750e4051441d4789998cd8fa6fb0ea26","cc7f21df969442ab9d37012cfc55a040","c04dd2efd63e48fd910e32adc6f2a78f","a67ce5d9edd34567b690cdc658c7d57c","b4f5472397214bc39e85c3dabeef6c34","7ad0a7b1e3cb4649a3be43086e3a4039","c9191774434a43cea8b43624958d2a0b","27d1265d5c204c45a25c597c6fdc02b6","60b1a1b64b8b46088d713a1034fec162","7c0173b8d3194dafaa5f0bc51e4db328","e86e4fd469444c82ba47e7fae34d5b7c","dcc6d50eb0ef4da19e6e34b5278500d5","bbdacface41740ef813e3e796e3a32ee","4621e93d887344d4a7f5c2174ce91cd3","ae6e5a296ffd491a9ae1272d91c8f43d","2183cd36bb34414086b6f5286c037ba0","f496e270e1cc4cf199da90587941ee5b","21c3e7acd3f94224a39f1642eb05d96b","86e4ebbe714a4c5cb9a2f894bd945458","88f1b9fd85d44436b96bc25e184968ad","404c6d6cae5d47aa889299a2dcf2183a","e09e682fa3a3489bba36db15e78c6908","35660e7cae8e4076add333cab265d6a7","4271464c61cd4c6fad23aa89efe2e2bf","d04df28c6c4143f2a47693bd049f2264","4f642efbef5b4ac8b3d87ac538442d93","d677cb2ac407425da06c364f7cef3cb8","522dd1e40cc14b758ac52de1e9fdd537","6021194a38774f22bb78a9d4ee75b3f3","43e219af1a9b41a693b7079153f1a682","bd601744ebd94895b2475a3efec88bde","ffef55600e2c4144be6a2e3a24368417","43fbc9ef60c5463cbd0d091a38e875c0","159312b57f5349cfab19e7c8d8fa695f","abda9c1a7101493999fbc372bc421d10","8d7ad34bcbbe4f7fa39fc31789a0f21a","1a43e161c0f244b1be2b292e3b8108ef","b01f6cb60b984ed2902fef0cc6421261","ab90f09cd1cb4ac292897e2b6ff2fa70","7b89c5b2c35c48a6938ac888c78f9898","b9868f9931214e5991e8190704523990","1acab52301c346b294948e1536fec528","f07c7e24ed67463f92937adcdd71f740","a6f5c7ff69354d6ea5cae087f25f0da7","986429ebebe946d0948d07431c6665a7","63d029e09e204b86862bfb74d2a4f312","12b21fc7f6674d1da30e8558ed5990af","8f8200599d86494a8c9b68cf6dabe178","042258976cfb4306bdcdaad9cf620121","16604173144446299a32f39a31f18efa","9a4e4cc72bbe4c699b6234f1be2a64b2","982530797a9844e5b6241099173af742","a317c9501bca415a933416783e77b585","0e5136d2e4744ca7a267f2fefdbdc2ab","510bf0b1782c4096b0f4c96c3780b5cc","1fff4443e34449dfb4d37d33bf3f28b6","2bb233054a8848c0ae527344fa86128d","9b4776f8a9954c928fe86007a10f5ef6","3c109dd3ddbb4235a773d664886f22f9","c7762c4caef447dfa986087d3e3ef328","ac2d2b46faf94a8f9bae9965f264baf3"]},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779160164031,"user_tz":-540,"elapsed":6811,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/762 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4cfee9edb86045dc8bc4f66e4c004711"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/353M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81832d31c786492ca65ee7ebca5592ad"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c9191774434a43cea8b43624958d2a0b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"21c3e7acd3f94224a39f1642eb05d96b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6021194a38774f22bb78a9d4ee75b3f3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7b89c5b2c35c48a6938ac888c78f9898"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9a4e4cc72bbe4c699b6234f1be2a64b2"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'generated_text': 'In this course, we will teach you how to master the commandline, using the CLI tools as they are used by everyone.\\n\\nAs your'},\n"," {'generated_text': 'In this course, we will teach you how to start developing and building your own custom tools in order to make your own customized tools that will make you'}]"]},"metadata":{},"execution_count":7}],"source":["from transformers import pipeline\n","\n","generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n","generator(\n","    \"In this course, we will teach you how to\",\n","    max_length=30,\n","    num_return_sequences=2,\n",")"]},{"cell_type":"code","source":["from transformers import pipeline\n","\n","generator = pipeline(\"text-generation\", model=\"gpt2\")\n","generator(\n","    \"Hello, I'm a language model,\",\n","    max_length=30,\n","    num_return_sequences=5,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":481,"referenced_widgets":["d8c8f0a3779445a4b5d7c4ce063e907e","7aeda635a9384fcb8443d007966706f3","a92066027a95461fb59af442ed421efa","47987f6fc0fc4ec788ff397a733c2a3e","41cc14a2f96d4f669c1e1817a0ae7878","cfd2b5cd26b14a7c9e8d0e2851919742","52f4667044334cdab871a782e6584136","179e6d31dbfb4f6b9e564d2946775798","60c5fe3d95734433a06813ccc9447f86","a92e0b9574b44e098452157c79094fff","11603427dde243a282b9040ee170d63d","e0041d71bdb94467bc3e035d83fbca1a","fcd5981e4bca44ca9d3b93a7b9f7a027","2e440d9167504886bd4304d9b5b35f18","8e75eb76d89b4000a43f11d87fbbefd6","a5bbf7106065442fa623ad0860530937","e6e27bcd13e24c51bd76a16512d30ac6","cd1dabedca914ce18132d471b7f4439a","dd3102b3e60640009b181fddefc02697","ecf4a5aed819457294142232f2b1f512","38d1e89184b94a1fbdaa3e3f406a3de1","5adb25ce3886417f96b8f0cd5e8e3bcd","f7bdd44d484745b7863dab9d261ebd42","4be797e9c9344faca04f0ccf6a8368f1","f8bac5c0ecc647a4a9fb7b67b07b0df1","55a4c0fb9b0f4a34b4080a55e2a6b421","8c70281d297a4d3c986a29bc0fe27b4e","fa28bc90cb244672acf43cbe22318f0b","5c202af08e464b3da201cde36db547fd","5ab94d03158249c69e63bf728061b351","5710834aa6bb4ad59a93beeb47db0652","8e8a6a710dae47278041bf1b18e8c8c3","2636157d270b42588895e90c0e7bc75c","db53f25ff98d49f5b0f762ba95d4c22d","4ab851ca09f84b7ba776f877fde81747","98b394e034bd43b284feed2b5d5641ec","d294ffeefaad4058b49e739a421f67f2","c76421d93cae4ae98eea760e283c119a","71bd1c1af93f4062ad653ae42c91ef53","660063c61fcc401db3a8c42627bc003d","aa4195dd80ff48a0b5ad2cf9b92c6016","90e083fa84a246da94c13c61b3975bf1","fac4587542b04b91a4643fd943cab302","a18f1afdc1c649be822370690073fd09","96a051d084614c52a8c808e8fdd9eddc","b4068de514bd4c1e8fb521505412083f","1401c10a748f449b9956b6968e0b9f7d","e591711d28f942afa51554aa8c818641","5fbd47073d094d1eabe2e89a29795a78","f466e5295fe24e9381a4743285b74254","54d98c59b2d94f1e9019db431c2ae89d","16d436c065f24369a846755f78cbcf55","dd90731a6710406391e9cec18e96a522","0e8ce6e278ec45ee8e139ae591740b9c","a162d50bcbad45918934d21377d124bd","0b17ed9b811a493d9eafee301eb2e240","0ec2ca0bb0824b6cab6c1ce92acf8bf6","f331fbb783f5481684c5df5400376cb8","0b9cce213b1040d98bfe873692f0d60f","8778e78a36c542fcab6830bb37a11f48","3aacf975d1c94a009d310b02621f7db3","27d8a949f2084961879ff464084ae067","7a9a4cd0e9074200a89456370dd8dc75","a7b1a408cb5a45919380024cb73a6afd","8606265954fa424a8a2bf81f7e3eb4dd","0034ae4fa8ba4249ab44b6029e34beee","27988b3d249a432b886b9d6e5d91ca04","022d302efe8946bbb1bc6815a8f5a815","f10348296972472293f4bb4257bfda1f","61bb0ef43cc64ac18a1cac2d86bbff47","5d009f7918f84c48b3afc759ce9dec6d","3b2a92ea228243ffa4e944810cf6be8e","4af9fab2e327480db7f0649a5cad4e3d","d4e52ba4b19744f4b8fdf200b6222bc0","368bc8bce7274b0e80e206ce1e1f9477","1fef965bf2ce4db1b8bfb9f30bc05924","df0a02e455d1420090ba589432ba221e"]},"id":"AB5hH1PuN6qF","executionInfo":{"status":"ok","timestamp":1779160359935,"user_tz":-540,"elapsed":10118,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"f6692877-cd5b-45e4-b0ac-1bd19729b2bd"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d8c8f0a3779445a4b5d7c4ce063e907e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e0041d71bdb94467bc3e035d83fbca1a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f7bdd44d484745b7863dab9d261ebd42"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"db53f25ff98d49f5b0f762ba95d4c22d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"96a051d084614c52a8c808e8fdd9eddc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0b17ed9b811a493d9eafee301eb2e240"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"27988b3d249a432b886b9d6e5d91ca04"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'generated_text': \"Hello, I'm a language model, and I was inspired by the language of Go and, I'm not, but one would think, could I\"},\n"," {'generated_text': \"Hello, I'm a language model, that's what I'm doing. Why would anyone say that I'm using it for syntax problems? Don't\"},\n"," {'generated_text': 'Hello, I\\'m a language model, not a machine but the machine is a model for something that\\'s human.\"\\n\\nThat\\'s an awesome fact'},\n"," {'generated_text': \"Hello, I'm a language model, for both you and me, this is why this project is such a pleasure and what I have found most interesting\"},\n"," {'generated_text': \"Hello, I'm a language model, you may be wondering how to do an implementation similar to that of Haskell. For this paper I'm looking at\"}]"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DKe8yyxVEQIC","outputId":"21c739f2-157a-4498-ea4d-191620c34ed7","colab":{"base_uri":"https://localhost:8080/","height":468,"referenced_widgets":["40edcfbbc4864962a682e38352ae070e","ebe18718873f4958bd38b98126ce3e96","60674f26df5f43f39ccaeb186e733a01","09e1f49fe52147e994da5cde7ab32055","76a0d81d242445b2b83488304371cbaa","bc8ffd8ce23f4996bbf03c556a10b99b","99c931659636466e8d7113b4c078baf0","b212acaadf83498b86ef3e4be548eac8","e631e2a68e074c85a8cea00025bc8e3a","1f5a68e8185d41b18debd78643eb7f50","ea763db96c224197a2e9daeb0f882002","b90db8ead9074b52bb57392afb433e94","b3d1838bc572499abffb02d7085f3624","d0eda505be6a4c9fa9eb9ba1d1ece764","52c1472314884441990385a9a82c447b","7e5dd6b568e948b187b600a2ab690368","d38ab335d88141739762947263b2f9a3","e6ce81a44a13481f962804ee82ea1fe1","515bfd9eb3a54fb798f56742500154c9","d64de99b0a934d7cb08e62e66f69f3df","dca4f51d71794edcaac1f8a6a9047ba6","b4110a48a808401696d8968536506e43","a964249b99434528a9223826fe38608e","58a70010892840a38598c9e68cd2bb31","eee2013aa6e84e588f7849487e85ad17","6d2b4f48c8a445eaa9098aeca760be67","3b224104d9f9401487d193011ab78715","4034e73180534a31969ca7c08ba2e3b8","32234a366b544015a66e7a3c6057226e","0099f0b8179f498ea9630625ae85adb4","3aa646bce19247db8191224c8ad4028f","75ea0297e43441818e540eb008a52a3d","32b8b5cc87f5491fb117abe1f6909862","cadbc8fecade4c1a91833d494ebe930f","c59a50a9ee144050ad3859dbddb78746","443e5cdaca3a42f6be3b3aa79756919a","c75fe2e5cbe04bf6adaf98e2bcca2a9c","4f403a8f01bd4a80b4fa8c0d730a539c","f5fe667c211e4a85bdfeee63eee9eb64","36e476aabf1a4fd6b57888f4a973fcf4","316f22c3db0a4a2db09ba298c3238e4f","0cc029cbad31473d83a0b7572812605c","39374fac3e4e4c98aa7a63f49b67d21f","b11f3dbaf6da4a14bf7a5a41a7614c33","5f23d2ce6e834eb89c20fc92aa10a7cb","9890c095b06949a2982918506ee1b76c","3fe7ea8e598546eab9c87f21dec8d037","464ac71c65f94b5cb3ffc2fcbab393ea","841250ba6f9b499a852f14bd04e51825","72a4485ff9324d09b4f7d1120c20fca8","d1dd607bdc194a8aa7865e0ed8c3644d","eff76cc7b2e84bf6901477b1dadd1008","8fe5cc20c37f4eb7a3bd295669f0831e","453aaa6dca4146a89cf2ccab89a17c5b","31e12e4d450f40c5b1a489f39d8b36f0","987a6961bb75499da24c4bc27da5d9e1","b7972ae5b2974f77af5953bf52a7f072","315155f7e4844f2ebb93e7f1899a7abb","e74bc4b60aa64d47a3b406ee2734f92f","75da82587ef748a1ad7e2bb4953a4465","a3bf9ea3f5504a078c5da245957b3de9","c1e7fcde43d04d60833806165fc7c71e","da8d0fa50eef4d239e312041aa9d3245","4aaec0c06a004f1bb71ffaeaa887e72f","268b14d0833b46b7acc944edd782242d","15bf198ce34a4feda5caf56217d557e5"]},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1779160702642,"user_tz":-540,"elapsed":10199,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to distilbert/distilroberta-base and revision ec58a5b (https://huggingface.co/distilbert/distilroberta-base).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/480 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"40edcfbbc4864962a682e38352ae070e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/331M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b90db8ead9074b52bb57392afb433e94"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at distilbert/distilroberta-base were not used when initializing RobertaForMaskedLM: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n","- This IS expected if you are initializing RobertaForMaskedLM 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 RobertaForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a964249b99434528a9223826fe38608e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cadbc8fecade4c1a91833d494ebe930f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f23d2ce6e834eb89c20fc92aa10a7cb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"987a6961bb75499da24c4bc27da5d9e1"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["[{'score': 0.19619765877723694,\n","  'token': 30412,\n","  'token_str': ' mathematical',\n","  'sequence': 'This course will teach you all about mathematical models.'},\n"," {'score': 0.040527310222387314,\n","  'token': 38163,\n","  'token_str': ' computational',\n","  'sequence': 'This course will teach you all about computational models.'}]"]},"metadata":{},"execution_count":9}],"source":["from transformers import pipeline\n","\n","unmasker = pipeline(\"fill-mask\")\n","unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"]},{"cell_type":"code","source":["unmasker = pipeline(\"fill-mask\", model=\"distilroberta-base\")\n","\n","man_result =  unmasker(\"The man worked as a <mask>\")\n","woman_result =  unmasker(\"The woman worked as a <mask>\")\n","\n","print(\"======남자====\")\n","for r in man_result:\n","  print(f\"{r['token_str']} : {r['score']:.4f}\")\n","\n","\n","print(\"======여자====\")\n","for r in woman_result:\n","  print(f\"{r['token_str']} : {r['score']:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e_YujDqzPofU","executionInfo":{"status":"ok","timestamp":1779161540515,"user_tz":-540,"elapsed":805,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"5a1f0371-0bc6-454e-c167-2c7d352cd31e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaForMaskedLM: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n","- This IS expected if you are initializing RobertaForMaskedLM 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 RobertaForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"stream","name":"stdout","text":["======남자====\n"," waiter : 0.1187\n"," waitress : 0.0790\n"," prostitute : 0.0621\n"," courier : 0.0592\n"," bartender : 0.0547\n","======여자====\n"," waitress : 0.2141\n"," prostitute : 0.0851\n"," waiter : 0.0718\n"," maid : 0.0654\n"," nurse : 0.0488\n"]}]},{"cell_type":"code","source":["unmasker = pipeline(\"fill-mask\", model=\"distilroberta-base\")\n","\n","man_result =  unmasker(\"The man hired as a <mask>\")\n","woman_result =  unmasker(\"The woman hired as a <mask>\")\n","\n","print(\"======남자====\")\n","for r in man_result:\n","  print(f\"{r['token_str']} : {r['score']:.4f}\")\n","\n","\n","print(\"======여자====\")\n","for r in woman_result:\n","  print(f\"{r['token_str']} : {r['score']:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lcVcfsQkTRsz","executionInfo":{"status":"ok","timestamp":1779161712060,"user_tz":-540,"elapsed":1214,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"17c9aa3d-0dd6-48a4-8255-098e5c4022c4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaForMaskedLM: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n","- This IS expected if you are initializing RobertaForMaskedLM 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 RobertaForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"stream","name":"stdout","text":["======남자====\n"," consultant : 0.1628\n"," lobbyist : 0.0517\n"," lawyer : 0.0293\n"," contractor : 0.0250\n"," mechanic : 0.0248\n","======여자====\n"," consultant : 0.1128\n"," prostitute : 0.0997\n"," lobbyist : 0.0356\n"," maid : 0.0346\n"," nurse : 0.0338\n"]}]},{"cell_type":"markdown","source":["- ner(Named Entity Recognition) : 개체(사람이름, 회사명, 지역명, 날짜....)명 인식\n","  - 핵심정보 추출\n","  | 분야 | 활용 |\n","  |------|------|\n","  |뉴스분석 | 인물/기업 추출 |\n","  |챗봇 | 핵심 정보 파악 |\n","  |검색엔진 | 기업명/지역명 분석 |\n","  |금융 | 회사명 추출 |\n","  |의료 | 질병 이름 추출 |\n","  |법률 문서 | 기관명 추출 |"],"metadata":{"id":"92FOBIy-U0Ka"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"tnSkCP4JEQID","outputId":"252c3746-0e20-4a3f-9075-696d76b11e6f","colab":{"base_uri":"https://localhost:8080/","height":606,"referenced_widgets":["75d148bd9e514a0a8a371bf7fa469f1b","9783b901bfde4229ac31e314c2e8d844","ed30bb8187884283bce1eae2eebaa745","bfbe247688574786a0756a506e19d476","447912220f1840adaecb6b151ca40bd6","8fde635a28454993aa05231660d5a400","dc98881b3cb0417ca20786d1d25e943d","58c6c808c3b743a7acd10d431ddf54cf","ab33d9b2008a4d4697accf7f8ffc8fa5","4c3389d46750438bb7e2f82998e7c24a","9510d465474442e2b936cd586146ff7b","5f5336ccde2144b8ad3deeaaec5f70c5","cc06623f748d4a9d8cba27e4f56fc259","6281327509c54411b5e1e945a8eb7ab2","de688d93349541aea879cfb6f25599e5","b588474d4c25430c9dfce3345b8319b1","1992e1938c3b479e99f43c19c749f936","9672936db69f4aeda7388d3e335e8494","29cafaef7ed14246ad9eef854420904d","f70eb078465b44fc8ec7752b9c45b7f3","59ab92fc2d8546b194faa9e8a789f190","57ba3554723c4cb0afc7b0809d536791","33e748591cd94828a6790a66045497ec","e5900edd3c6e4ba88e73ad060c6c40ac","a89fef6b1726459e8ee1a645cd684734","fc294e654bfd46259abd2bbe8738a28a","551d019dd9be4cd98d996a15fe91e15a","cca787e882c341d984332d986759fbaa","fb88fd1fdcb742a49e1079848c26d3b0","73dda4a877944c4f89924ca2537eb39b","42dcb80e64334e029d77612aba307bca","6af0c67c37694f039c59505b1b8d87b2","167515517d8d4892a69f61f55f4fe55d","fcd5ea07f2564aad9d8e0f2cc378b01f","49d2581fe08c4ffaa97836a56e582d91","66175b0fb1154c9488e733ac6421941c","3a701387fc824239a2d881a89cf133a0","4580192ef37c49d081182666c9a878fa","6cfdca1dc8b24bfdb28010c87dc43fa5","270c9d10778842cfb5ef710c480ae54f","7cb1106969394264ba0f36a98db0db14","9cc48a3e1fa94ec8a1de8698c5f017c9","6cb2f2fc052947b3ba0ac80383fa4d8d","c0d7203177c845f08974c5bd6cf003e5"]},"executionInfo":{"status":"ok","timestamp":1779162439663,"user_tz":-540,"elapsed":28578,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/998 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"75d148bd9e514a0a8a371bf7fa469f1b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/1.33G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f5336ccde2144b8ad3deeaaec5f70c5"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n","- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/60.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"33e748591cd94828a6790a66045497ec"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fcd5ea07f2564aad9d8e0f2cc378b01f"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/transformers/pipelines/token_classification.py:168: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"AggregationStrategy.SIMPLE\"` instead.\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'entity_group': 'PER',\n","  'score': np.float32(0.9981694),\n","  'word': 'Sylvain',\n","  'start': 11,\n","  'end': 18},\n"," {'entity_group': 'ORG',\n","  'score': np.float32(0.9796019),\n","  'word': 'Hugging Face',\n","  'start': 33,\n","  'end': 45},\n"," {'entity_group': 'LOC',\n","  'score': np.float32(0.9932106),\n","  'word': 'Brooklyn',\n","  'start': 49,\n","  'end': 57}]"]},"metadata":{},"execution_count":13}],"source":["from transformers import pipeline\n","\n","ner = pipeline(\"ner\", grouped_entities=True)\n","ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U2MlZ4o5EQID","outputId":"9f2040bc-59b8-4f2f-9057-2e19f5d1a4cf","colab":{"base_uri":"https://localhost:8080/","height":252,"referenced_widgets":["6dacda457d664d3bba2c9565a0783bf7","b6064854a0c740d9b7c88ee948d80d40","c3f5045322a941c6becb1bd1d3af02fe","255593be7696428a91f72c82147fa1a9","3a435e33c4964cb6b69b7f5220c1733c","d35e1493e41a4ba5a7c942b74ad2441c","3aed118697424b13a44c60e0dba2551c","029db81cad94473ea4afaee8c5d75ab5","b24e236b147241dcbb700aad08a51a29","8edc318a807649e89216cd525b6157fb","5046f52f0d56477e88498cffd52bd9d6","99ec7875a03c4a7b99747da6a8b661ef","d6b795dcd2e0459c8d3f357ca4b0519f","3046e741214f4ca6af6523b558c1f3ff","bffe4ae41bb74faca27e2ce178f615f4","5e9f2f8422e140128e71025016929fb6","9fe9bdb6ba5040a1aed01e2c2cf2e7ff","547d71fa5ff84c1e92e7cea78014962e","cf28d267657941768b3a7ce24ffca58a","bbc9f965d3ae42ec8b8e472d312c6367","0c86199e594c41faa8607fd2b483ecc0","910134cacbdc4993b891062a4bf2e3d9","e6c113cd38e94fdcb775ce98751a1338","bef4ea8edfd64b74a3bc6a7338231b9d","f4565f8ae19c44ff81eb5d88035a2ef8","789f054fde8b4377af4609149250d664","ff1fb2d3a89e4521b26c1af1c6e76bec","0b7c7d56b5314c7f9f4ec32b68bcae29","b1d63248e1bc4d448c5f287bad445577","3aefa7eca25d483bb330993624d97a4e","160b6303e00a42889c94960897f1baac","927c81c9934543348a96ebd348f8a469","f6f218cc22af4c0995fd9d29fa7646a4","a4c123f4f1f24dffab14e0970c247f21","89390b0ed8184e9586ed96cac9af2a83","31d63d33e69b4386bba0bc4e20806a69","cc6bbd0aaeba4246aed4f5d3167739fe","5000587b6db34d04a9320cf0565874ae","ed292cbc901649b2b40a092a4de6d8e5","8b203543876f420fad2e8b8d40a520a4","82eb5e13fc2f4e4d95585c99653ffaab","35e17c3e382d46ab920f6d24080e2ba9","00239bd770744bfab96456cb15f764cc","77eff5b6e9d54a7fbd1791555f27378b","74e6604fcbde43e9852cfc9930ffec0d","0c00f2d00555489f9070ef291bc21be8","ec24ba09e39e4709a37b696d4acc7cfd","3589556e3fc44a539b5cab245250d990","cabaa9c15c5e47ad8d95b9002b65378d","085a5d2b4fbf4e2d83f64db4af44bdc5","0014f762f9f7437a96459a0e154b4ffd","a0f5cf8feeb1433f8ab9f188cfec45ed","fc53f040bbb14d11ae09c5be78ccc1a5","1a9220c76dc04085af83741d423283c3","cf58cfd7a26e4b25b89273dfaeccb147"]},"executionInfo":{"status":"ok","timestamp":1779162607479,"user_tz":-540,"elapsed":7220,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to distilbert/distilbert-base-cased-distilled-squad and revision 626af31 (https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/473 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6dacda457d664d3bba2c9565a0783bf7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/261M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"99ec7875a03c4a7b99747da6a8b661ef"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e6c113cd38e94fdcb775ce98751a1338"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a4c123f4f1f24dffab14e0970c247f21"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"74e6604fcbde43e9852cfc9930ffec0d"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'score': 0.6949766278266907, 'start': 33, 'end': 45, 'answer': 'Hugging Face'}"]},"metadata":{},"execution_count":14}],"source":["from transformers import pipeline\n","\n","question_answerer = pipeline(\"question-answering\")\n","question_answerer(\n","    question=\"Where do I work?\",\n","    context=\"My name is Sylvain and I work at Hugging Face in Brooklyn\",\n",")"]},{"cell_type":"code","source":["from transformers import pipeline\n","question_answerer = pipeline(\"question-answering\", model='distilbert-base-cased-distilled-squad')\n","\n","context = r\"\"\"\n","Extractive Question Answering is the task of extracting an answer from a text given a question. An example     of a\n","question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune\n","a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.\n","\"\"\"\n","\n","result = question_answerer(question=\"What is a good example of a question answering dataset?\",     context=context)\n","print(\n","f\"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\"\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":195,"referenced_widgets":["9be53c3b17694abc8962fd0464b62edc","3952d5ea8fcb4eddadc99be3062314ee","7f6a6af882d849b5bbc455fcce7f7a6c","f423a9bc3a0b4c4a9f561aa419b80d1f","fa7fed4283384f5fbe3e90708f734466","e5b645eb9faa4655994f5366205344e8","fecdc24f0fbb4c7fbf2b75c55fb006f2","d53bace3aa704683857b95a71bb4775d","307310b8637849e7bfe399cc34b52d09","56686b3e3874427f8b2e715f65a52411","46c674660a2f469bb1f28d454ca7eada","03de5ced498546a5b3875420623997c5","b9a5aecd33c04685a2f43ac71ade21bf","83f67b9974ac4e079ec251c5cc91b826","de83fde2c87741078f58e67f8ef0253d","e80532c37eaf4d9fb45cb35045017ffc","88930e08fc004c888232642a502e90a4","60d230a9066647a9ab202599ec386699","94cf8871a84f45a29ea11832842f2b86","118a0cff0d5f47a0b10b79fc2276886b","9a46da0060584c03b86dedc041d4fee0","bbab2e43b4284b7f9ddaaabfa46b9786","99af8100890742368f35cf0bdab88520","da10794b99ec4bf8b9c033fb9d9e7bc1","e4de9b28a4d34044ac2d3a9194260284","ae66024e7d0b4312a3eb82b4c7fbe0bb","f1bf14dd89db4177bd42aa52abdef959","0d51c93a4c08434fbc78f1d8f22c2ce5","02e708ea9da343ab88893bd821c9826b","977f03b3550e42ea8e940c0a4e27f1c6","b6d4b8f2a52040188a80567ea7fc6e0f","1dd07b3819eb4072984b4e65af1b7e0b","fd42e8123e804c4db5af78c7caa20299","93354b09c0d14b5f9cd386fe5f8ae9b6","8499ed2179934eb09f46772c84c74b7f","e13158e85a9c4a70add9a497731bf2d6","4fb69da4ecca47ed9806dc5baca25f9f","0c1451bb71a544d5801580a1e6dff11b","930d77dceb494d0eab46eb6f7379c36b","ee42c5ac936e445899c78cd40caa910a","1ee19fad122d4222a6e5b8efe901de4b","6243842db2264b709bbd14a73e7aa715","611529abc7ac42efa19456421e232c87","003fa97965bc49fb9263c6a810955be5","ac8113e2aab744abad389bc20d5cf5c4","fdb84462c5fc4ef59b889ad57b3c2b68","afe0acb42b884cb3924c0f6bfe7f668c","7d22073da60e4a91a660192348570de5","a5ab71b357b74b51b56abf95a5f7e58d","60ef74b6eb384cdcbae663b49adadc04","c478ebce827f41fc8862a4e77dd22f3d","cf505cac462742ef8aebd5b458dba24c","c65d3d99182641a08884955ec0182170","f09abfe56d034c3f99025812a2b07f88","a33999ca0ddc45a194ea53ed0de48c76"]},"id":"85kDcd_FX83j","executionInfo":{"status":"ok","timestamp":1779162975333,"user_tz":-540,"elapsed":8954,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"2ebdd1b4-b2c3-451c-89c4-aded62111c54"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/473 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9be53c3b17694abc8962fd0464b62edc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/261M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"03de5ced498546a5b3875420623997c5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"99af8100890742368f35cf0bdab88520"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"93354b09c0d14b5f9cd386fe5f8ae9b6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac8113e2aab744abad389bc20d5cf5c4"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Answer: 'SQuAD dataset', score: 0.5152, start: 151, end: 164\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zb9GiI98EQID","outputId":"d827ef69-5ba5-4fe0-acb5-80e2a6c97eb7","colab":{"base_uri":"https://localhost:8080/","height":326,"referenced_widgets":["0458e72a30d94899840f93d9d6dc6ab7","f579fff4aa464b49843357b8426ba46b","281b61f74c1d4962837c55df5e61ee17","13bf5168c16e46a7b7a50c2f4f04b11f","60e0c13fdd8f47aeb2f30f4642bcd1fb","367a7b8e14ef40b3a4cb2efd5cd46ee1","78db78b0ae204ed6ac771ccf1beb0bcc","6c6f7602b9934832a244edb3c9ab01ac","7645de680e72424a84e4bf7b84972525","dddfbacd7af1416488bb0380fb78c45d","94d972910d544f17951702604f75158d","cba7c4d4e7614f7aa9b1796fa286fdb6","a23b74de6f3340f7ad3cb2d52d35effb","7ffae42fe92740f981452890502a75de","41357b53451442e7a430054eee9b75dd","61f19444b8a8408ca5e14cd7992077bb","d5e0f880e77244829bd634e2d890d2ec","52fe2383a08649e2bfe729de42f08473","b236d65d19164e1d8f124cc3a6aec987","77f6f8c9bc9940adaf1aea0f9c253366","59366cbd2906424e9cd980b6da4f6abc","1a0e5acaf7614b35b740a1c909c9c41a","018f9e7d39c64d76a028a5a57cf742f9","51cf987559924a61b8d44482fcc34989","7447d1fc2daf41ba8be1a849e06ffcd2","c4afd7b57c0d4593adfdd5d62c00da35","826e8950c50d4d1d8701d31a4a39dd63","c7caa771660545d48f18b73448c86806","2ff4c623fcf44645997dcc73e5d60c20","07e331f174ad40e198ae366cb5d7a184","c166d862cff54332b331b70a966b51a1","54e2ca43278d46cd80ac0b922e8bd732","5243e10ad61a471ab39d5b1b9236e082","72e3ee0d446b4eaca1f0b2eabcf5a5f4","1ed2a3a6533c4d2498fd36f4b13d763d","0ccdd54d092248e0a611ecb973b67195","adfb79c6cc89448ea82068d21bc472f4","f7d207dc05bd40c7b3c4972d5032af8e","2b969890716e4d1f88efbf4146cb8b7b","fbb6f082ec84453283f0857b7285a86f","b46fde34cb154312a96497cc790cea06","b062b3671f824253b9b464c169fd3db2","d26c3742d4ff474298062102135c1109","026a04ec57e242c0a71714c0de3ed536","09b1b4f3b4174bd3b3279837ccc2e8c2","a62bd5ca0fd0476e8f725588eabdfd07","2da57fb4254142fc836f89a0ccc5aa45","b6eb756a389b44e7913281a12b6aaf21","3d1960811d494cbd964991e229bec891","0a10d00ca6354ee3983eb6ecebc6bf31","bae04ae1f8854a059e856dc534d9517e","1caaf6378c804639996f52b986d626c8","113ad7f3788845289930f355bbb41e2b","244707902f59425eaf8097890714bd50","b5d8e6cd6fa44e82953960a442b4d99a"]},"executionInfo":{"status":"ok","timestamp":1779163075049,"user_tz":-540,"elapsed":45449,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0458e72a30d94899840f93d9d6dc6ab7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/1.22G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cba7c4d4e7614f7aa9b1796fa286fdb6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"018f9e7d39c64d76a028a5a57cf742f9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"72e3ee0d446b4eaca1f0b2eabcf5a5f4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"09b1b4f3b4174bd3b3279837ccc2e8c2"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["[{'summary_text': ' The number of engineering graduates in the United States has declined in recent years . China and India graduate six and eight times as many traditional engineers as the U.S. does . Rapidly developing economies such as China continue to encourage and advance the teaching of engineering . There are declining offerings in engineering subjects dealing with infrastructure, infrastructure, the environment, and related issues .'}]"]},"metadata":{},"execution_count":16}],"source":["from transformers import pipeline\n","\n","# sshleifer/distilbart-cnn-12-6\n","summarizer = pipeline(\"summarization\")\n","summarizer(\n","    \"\"\"\n","    America has changed dramatically during recent years. Not only has the number of\n","    graduates in traditional engineering disciplines such as mechanical, civil,\n","    electrical, chemical, and aeronautical engineering declined, but in most of\n","    the premier American universities engineering curricula now concentrate on\n","    and encourage largely the study of engineering science. As a result, there\n","    are declining offerings in engineering subjects dealing with infrastructure,\n","    the environment, and related issues, and greater concentration on high\n","    technology subjects, largely supporting increasingly complex scientific\n","    developments. While the latter is important, it should not be at the expense\n","    of more traditional engineering.\n","\n","    Rapidly developing economies such as China and India, as well as other\n","    industrial countries in Europe and Asia, continue to encourage and advance\n","    the teaching of engineering. Both China and India, respectively, graduate\n","    six and eight times as many traditional engineers as does the United States.\n","    Other industrial countries at minimum maintain their output, while America\n","    suffers an increasingly serious decline in the number of engineering graduates\n","    and a lack of well-educated engineers.\n","\"\"\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pYpUNz0KEQIE","outputId":"3e3f15e0-73bd-411f-92b4-de15b94e3f57","colab":{"base_uri":"https://localhost:8080/","height":316,"referenced_widgets":["0453de2f929a447eb4e10662fc14b097","f69773ab0dbd413c856006bdf226cf4f","48b8ca56db5143fe968d8eee83ab90cd","c727bd560bcc4b4ba1b11865f0d40a66","c5f7802a1901454b81b9475f70fd0fd8","93c0eeb68fae4bf69ae624f5e2ca90c0","3c658bbc31614b57bc0f6ca6e70f52bf","8e65d5a8940f4b4ca628f524037ff506","e8089f3690f74ac5a9649cb43cdf858b","06e8da2d4d894b2883ad0062cedbce9e","07faad8975e444d0937bafdba3a19d12","ed0ce79c47444deea8b75a4da8151e73","214bfbb4b0c04714868f4327c027d081","40a946239f964892adac954b721c8603","fed8fd4c1a344765a87a1d02e32872c2","85cc8fc2cd3b47d180841484a24037a9","effb7a5fbe1849e98962adf734362f92","7a8e3ed038184a01a274ec2ce9829410","02658b1687d04bceb99880935bc7146b","df8d531b6c254116b59bc73917084f1e","c2af608aa9f742e987bba68926652af5","c53b859292f54d78a39a91fca9d56aa2","231513690a364338bcb23b472f8fb522","198e67c154c644e68bafa1195b961d92","7adade350ac74062901caa13fb62d697","8d82e22936c14c3c8332bed8a6735132","a2812505b229433a90200780a4461835","3ee80f005c954f83af4bfc0a109d0355","8a2fb26cb6e549d8a96fe5050595a64e","85162f77c5c64cca9893b9524f796c69","400a51d4977b4ed68608f12e276f63cf","93d669ead1f54b39a26be6ab484fb771","502d916b8cdb4a7bb9b90f208df3fbb6","69415361bfdb4a9190081fb9f28b2f18","7b1ac9d6939147228188ebf85cc6519d","9648ac8c17a74da9be20dd51e4880ff9","c923e6fd1a8944518e77deb83fe11bf7","013bb73da1064ae7adf1ec1730fa6779","54673cba6768498ca112c2a9a71a8f5d","2637b733fd084fdf8e9973b09ed07914","eff0847a1f344b0daf64e543b2d10dc1","11ed9123cb034c909bbe8e45eafe5b84","192a2467e4944075a314676bf3055764","45adace974ef4f939ffd44fa4688437c","2300eff08e0f4c2da9104421c4ae4024","40cde3e511eb4283a78e7f57959c0413","97fd4fc12d874997ba75102f9729bf8e","16f909de931240fe82bf54803ecef703","b958e352d2d34f86807ac61192a393cf","311543b186de48b4b15a55ef43a72ec7","e6f69aff8db045e299cf7d08dfb15d10","000b7532322a4b308a896f180c40ccbe","c7ffca57df2f40fe9553f1ec3f5b3a63","e7234c7419fb4c809950975dfafc1b7c","4cd81d5f05a144878fe6440433a1e063","62804b008fbc499e83441ce18cfee7bb","ca392aa5e3eb4c20b86bcb081af40010","c2fd8a36c8614f09b1c77ed8fb902360","ee1dba405c184190b75b54fa723601b8","d1380ee006584f3db6d375c05f868345","7347c29520564db1b6786f4e2bbbb88a","6268c26806aa4d9381ebd56dac421f7b","7ecc1340fd9841729c0429a8a3c8b264","13fcd40183c64030a7557a9983806960","98a3271d8f95422faaa85e274001cf5a","71ae4804601e4f379e0ae836cb2954df","a8447d70ff3b4977a09f3ca3a8edbb24","5558dab834604dcb94f9827d6b621bd4","7865470715114590a8d87637cbcbeb24","26c0cda68ee145e093e1dd015fb4965f","bc7b9495753c40e78f86bf2d992a17b6","9078c8e4032e44b3b12ef0aa0a68d00c","df51737274fa458392ceeb04da5b4cb5","05abe3dbba81497b801ae5a50ffce6b3","77546c7ce53f458a96234a9a3dddfab6","cfc8e74df0f8496d8ee016ef7e282a23","9838d7101e2e4faa84fa1896ebf3f69c"]},"executionInfo":{"status":"ok","timestamp":1779163186046,"user_tz":-540,"elapsed":9826,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0453de2f929a447eb4e10662fc14b097"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/301M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ed0ce79c47444deea8b75a4da8151e73"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"231513690a364338bcb23b472f8fb522"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"69415361bfdb4a9190081fb9f28b2f18"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["source.spm:   0%|          | 0.00/802k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2300eff08e0f4c2da9104421c4ae4024"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["target.spm:   0%|          | 0.00/778k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"62804b008fbc499e83441ce18cfee7bb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a8447d70ff3b4977a09f3ca3a8edbb24"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n","  warnings.warn(\"Recommended: pip install sacremoses.\")\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'translation_text': 'This course is produced by Hugging Face.'}]"]},"metadata":{},"execution_count":17}],"source":["from transformers import pipeline\n","\n","translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n","translator(\"Ce cours est produit par Hugging Face.\")"]},{"cell_type":"markdown","source":["### 멀티모달\n","- 여러 종류의 데이터를 처리하는 AI\n","  - 텍스트 + 이미지\n","  - 음성 + 텍스트\n","  - 영상 + 텍스트\n","  - 이미지 + 텍스트"],"metadata":{"id":"IBC3oYPABgSu"}},{"cell_type":"code","source":["# 코랩에서 개인 구글 드라이브를 /content/gdrive 이름으로 연결\n","\n","from google.colab import drive\n","\n","drive.mount(\"/content/gdrive\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-74kanNvBf_P","executionInfo":{"status":"ok","timestamp":1779243893567,"user_tz":-540,"elapsed":23909,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"a7562c03-1f39-432d-b171-b10d78d22684"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["from transformers import pipeline\n","from PIL import Image\n","\n","# 기본 google/vit-base-patch16-224\n","classifier = pipeline(\"image-classification\")\n","\n","# 구글드라이브에 있는 이미지 가져오기\n","image = Image.open(\"/content/gdrive/MyDrive/Colab Notebooks/jeju.jpg\")\n","\n","result = classifier(image)\n","result"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"1fMWLksHDGh2","executionInfo":{"status":"ok","timestamp":1779241893944,"user_tz":-540,"elapsed":3675,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"a1858a41-a151-46e4-f98d-080b1bccb0dd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to google/vit-base-patch16-224 and revision 5dca96d (https://huggingface.co/google/vit-base-patch16-224).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'label': 'seashore, coast, seacoast, sea-coast',\n","  'score': 0.3017870783805847},\n"," {'label': 'promontory, headland, head, foreland',\n","  'score': 0.2822805941104889},\n"," {'label': 'lakeside, lakeshore', 'score': 0.09529954940080643},\n"," {'label': 'coral reef', 'score': 0.08251819014549255},\n"," {'label': 'sandbar, sand bar', 'score': 0.03217768296599388}]"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["from transformers import pipeline\n","from PIL import Image\n","\n","# 이미지 캡션\n","# ydshieh/vit-gpt2-coco-en\n","captioner = pipeline(\"image-to-text\")\n","\n","# 구글드라이브에 있는 이미지 가져오기\n","image = Image.open(\"/content/gdrive/MyDrive/Colab Notebooks/jeju.jpg\")\n","\n","result = captioner(image)\n","# a statue of a man sitting on top of a rock\n","result[0]['generated_text']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"gZwNkZivFUU2","executionInfo":{"status":"ok","timestamp":1779242172258,"user_tz":-540,"elapsed":8819,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"a7aa4897-c2d8-44f0-ee84-740f24c06619"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No model was supplied, defaulted to ydshieh/vit-gpt2-coco-en and revision 65636df (https://huggingface.co/ydshieh/vit-gpt2-coco-en).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:949: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.12/dist-packages/transformers/generation/utils.py:1168: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["'a statue of a man sitting on top of a rock '"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","source":["### Stable Diffusion\n","\n","- 텍스트 => 이미지\n","  - 노이즈 -> 텍스트 의미 이해 -> 노이즈 조금씩 제거 -> 이미지 완성\n","- GPU 필요\n","- 코랩에서는 런타임유형 변경(T4 GPU)"],"metadata":{"id":"TVXBFlx0G35k"}},{"cell_type":"code","source":["!pip install diffusers==0.29.2 peft==0.11.1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"XlVCTPwDG0Hl","executionInfo":{"status":"ok","timestamp":1779242901698,"user_tz":-540,"elapsed":7315,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"22bbdbe0-ccaa-47ba-ce7a-c76defcf54e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting diffusers==0.29.2\n","  Downloading diffusers-0.29.2-py3-none-any.whl.metadata (19 kB)\n","Collecting peft==0.11.1\n","  Downloading peft-0.11.1-py3-none-any.whl.metadata (13 kB)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (8.7.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (3.29.0)\n","Requirement already satisfied: huggingface-hub>=0.23.2 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (0.36.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (2.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (2025.11.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (2.32.4)\n","Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (0.7.0)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.12/dist-packages (from diffusers==0.29.2) (11.3.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from peft==0.11.1) (26.1)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from peft==0.11.1) (5.9.5)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.12/dist-packages (from peft==0.11.1) (6.0.3)\n","Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.12/dist-packages (from peft==0.11.1) (2.10.0+cu128)\n","Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (from peft==0.11.1) (4.41.2)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from peft==0.11.1) (4.67.3)\n","Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.12/dist-packages (from peft==0.11.1) (1.13.0)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.23.2->diffusers==0.29.2) (2025.3.0)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.23.2->diffusers==0.29.2) (1.4.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.23.2->diffusers==0.29.2) (4.15.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft==0.11.1) (75.2.0)\n","Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft==0.11.1) (1.14.0)\n","Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft==0.11.1) (3.6.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from 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/usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.29.2) (3.4.7)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.29.2) (3.13)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.29.2) (2.5.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->diffusers==0.29.2) (2026.4.22)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.12/dist-packages (from transformers->peft==0.11.1) (0.19.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.13.0->peft==0.11.1) (1.3.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.13.0->peft==0.11.1) (3.0.3)\n","Downloading diffusers-0.29.2-py3-none-any.whl (2.2 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StableDiffusionPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\",\n","                                               torch_dtype=torch.float16)\n","\n","pipe = pipe.to('cuda')\n","image = pipe(\"서울의 밤 풍경\").images[0]\n","\n","image.save(\"/content/gdrive/MyDrive/Colab Notebooks/seoul.png\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["b208fee738674c0892de04f6e6a11545","4747e5b4e0d6462caeaf2005ac08b018","d0a2c739f7d24c4e908bc024127e74f7","ab4648193d4843ef99ee8fa60db52ae6","7db7797f8f8b4ae495ef7000133b69a8","5e15497412fe43a68ce6d7df6558699d","80e7ea5745f14f2ba9aca8827b406821","1621c9baee99462ca931b5990f94ead5","c5513262f4db43e4a87792bd7fd52159","f956ffc8ae7946b5baea7ff21b6a75b2","77d5b0c1351241829fa0f554784b5a56","7631a84130194192b9c7dd42c0769f1b","74078d55435c4a5789a3babdc2e01e83","b9ed52d3e7db45ebabd5208003a077ee","7d7154334e4a4c338623878520f7db41","a2efdba8299c45b2a8cd7a68af4edf7b","7ef1a340f50d4664a046a4da6b7af947","ffa8ded0e9d0495fba68e8dae81db354","c47e16fea70e43348c2572971b31e709","0282b4a1a90242b2b436e18acc6b59c3","3bd8ade157e941d7af77de32cb038071","ef0f340cb30448049ed873365736c621"]},"id":"X_brI7e1JNXk","executionInfo":{"status":"ok","timestamp":1779243931616,"user_tz":-540,"elapsed":31960,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"47ea08b1-2a86-4774-e465-249e728bfee2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b208fee738674c0892de04f6e6a11545"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/50 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7631a84130194192b9c7dd42c0769f1b"}},"metadata":{}}]},{"cell_type":"markdown","source":["### 오디오\n","- 음성 -> 텍스트\n","- 텍스트 -> 음성\n","- 음악 생성"],"metadata":{"id":"xi8NypnMJRe8"}},{"cell_type":"code","source":["# 음성인식 : 음성 -> 텍스트\n","\n","from transformers import pipeline\n","\n","whisper = pipeline(\"automatic-speech-recognition\",model=\"openai/whisper-base\")\n","result = whisper(\"/content/gdrive/MyDrive/Colab Notebooks/obama.mp3\",return_timestamps=True)\n","\n","print(result['text'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":443,"referenced_widgets":["dbf2ff7e07924a54a4e05e20250271b3","f4bc87b192ef4ccda290ea20d4e7d1f6","ed1e537a180241649df0e7b27475f033","a466799f665c4c80bf12eec22482d530","b7b03be3e11646428939a458394219a0","276886d7e4c84ae5be0ac4f01d51afa0","f8f6eb598bd7428cb55c98deb9fbf484","2801c2735503480882db2052b4a67a06","cb8919db67184c5f9ab227c24df782b1","ba04886e786045caa0946e919169ce79","c897f0f672614bc79f3649b4ae4a5625","d39ef6edd9aa4776885255b70494eae8","b4e1ded5981946329120c5c047d4fc15","efa1cfc993974fcbb9beb5ff0642d3b9","3ab395fe74da40f38d7316712e0f912b","66110ae77d2241889eb7317605c31e2c","d4ec90942938453b8f3c9655ddf07da4","b229cc7adac44024bc251bb8a356db47","b5f591419e5c4071956d62f1df902bf4","20e24a453804493f84e6f9e1a7c85b65","82eed61392384cdcae3fd11613c22425","0fcd627cddac4c80b711160d2ebc7070","9772092a96874febb7f4c0ee1ae476ec","53a7125fcab0405a951f8b70ef7f9bba","cf16cee711f34979aac3d9ff9f1176e3","6c83a2cf27d44097b2c877c463ef88d7","fdbc84ec78de4e2f98f249920cff0393","0e01175ecb8d424f8bab376e25abe40f","bc9e2cef4a4c4ce9b9c5d5b436397958","99bcc1251982422ca2046337ff7bf73b","254a6bebe76b4cd38647e4ec63dd1888","501cc9a7829f4e2aa688a173245f0d42","4f4afc9e22c24bce9e467d68357da1ac","143cd606fbca462b9faa79bab31836c1","1ecd757ad3284c67ae6346ccb0f9ca04","9a26674db11e490186011f579a03fc05","2c867d26b6d64821a00818e9954e844a","da863c52add74365a61487b24b7a58d6","22bb351879c64afeb576ba43e8badfc1","f49a0ae00edf475fb1cd192c20a85911","65d75b285c5c4c90b55ba078c1f43cbc","f2c38d3e40b240e0833ac615c7eb2def","661f557bfd0241e3aaf236714692b24b","e40a9f74877645ca9010b42073dbb3bd","4cb5a61cc0ce40bf8de08f03277c29b3","aa4fb1c2fe0d41f9a763393a86fbc5fd","a72d73039e574515beede09279e56565","7ed8d6bb58d0497ab9f25a9f8ff99e20","c4d196b26a314e4ab61fee2b6549e72e","41f8c8a482584f48b70d893c354d3575","d293409a61cb46c483e1ece3ea590ff3","d5c76e6e81ab47e0b5da9e0c298fbeef","1bc1639ffce044faa4526f21f93b8c15","fb2505c1ec514538ae62e7fefb3aa168","2ebe3952f61649e7adf5d6165b467f44","25a7ae6d8e834e2a9c07da5a3d49d457","8174229b3cd546de86b5f717e6070d97","8c116cd8be544036bee5ade6b655adea","51d4ac71d7a64ed6a7b49013540df830","ee97d1f1f2074a0ab3be0ba7cf024d36","58adcfb6ae49425eb64a2ce886e71a97","18c7d7a4b3ba4d41b94e66fcebdb0eeb","e3a1ecdb173945388bff467bf45714e0","830063f460814d2c9344a8823a051cc3","e128076cfd1f48efae614c207e482261","22952a3d4cab458582e8cfe5ca98483d","da7a342306094559b5aa460aee73ed89","4e38ad22ee244283a423bb614782d692","7eae71e96927439b93dbd463ce67c9fc","da991f5ea4dd40198f14b516701d085f","c152ed0d58bf418a8374768d3c8c54db","79da3cce8bf242d3a20234c03d00bce3","c890bd11bee24869bcf48a0186bfc16e","7593c664185f4c05b08209fc087c0907","ca4348943d124b1a9b03fbed103f784c","cddfb458628d42cf938915df8162bedc","c921f153fc464b1a9b4206de030a12f0","7f43ef7ee44d46dd814ac97e5a422ce7","e94ffc3297504c0c84ff87b06e84080b","808b9db3879e4fa88dc6c5921a9cab52","52640a5f30724c2abf7ae132fa644e92","672bd2105b7d47d5a619c5001ed09833","864c0bc545674df7818318ed2508118f","91359df19cfd44f593a196cd38f59d3f","15381d30a1a24f358495d0da0d702a4f","625e55c6c354421b881ddf391b90fc18","db30cdeb162b461a901b0c9da8d381e5","3f80693eb49c4e2183470dec4f4e669c","c434d8fd380b4eb1b4a5a512a38d1744","de24f99421a14b6ab148a021d5d23b6a","2a37b52ad8664b6fa4b2c9962c86d83f","9f7e8a91b3ac49c596cdc84563a373b6","d6e67f86afce45b0840cf91af16d18c3","6ed3f094384e43ef9d6aafbaab1e3843","c0782b779a9240eaa98c02940a63b0fd","94ed49b55276429b914a3b51ab0b7854","9f9483ee901942fcb329012af0697993","406e683a83f2475da57eed3316c45420","586f73300d62449f9b537b147d33ae3a","5ec7979296e84ed285b0de3f45d57ba8","506f2ead15e84611a0a7b52e00787bab","fb498fc56c864b6ca4008c3566ed6464","25c806f3486b46c2a2da4e839c5a75bd","9c05be310ff240cf9cf1d43a059560b2","42616ca3d10b48a1b1ee485e68748ce1","3100b34fdd644d4c80fbce29f4848590","c32fe6c2d4004f3dbe9c29b964678479","32b391fae04d4fd499bb8994fa464830","4ba4405407ef43738a50f02d1549c3c5","aa19e3e6b84c44de95620d23035bdc99","e2a1d08a93a843fa9e2883c5d54d2138","0726882162ce46f2b790d8f8a348191e","6e7c9ff07e9c4cdaa08d413d5f164725","c007ac561c024d1ca069465f97121b1e","0db7a2a2735842b2a6775e755013d781","526c688892f547cdae8d3001782361bd","2f1b8f0624a646d9b99f02cadf709503","6487ae1b9aba457fbe0754f621a98721","875348d5f8ae4d40852180eef1d74c9a","3a6668981dff41a4b149a259b14bd4cc","b2b6ad37c91a475a9bc2fe7a7286850a"]},"collapsed":true,"id":"wVNmxezeJQ-7","executionInfo":{"status":"ok","timestamp":1779245412685,"user_tz":-540,"elapsed":38238,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"7196a972-d52d-4e59-9c0a-60a755ff9a26"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dbf2ff7e07924a54a4e05e20250271b3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/290M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d39ef6edd9aa4776885255b70494eae8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9772092a96874febb7f4c0ee1ae476ec"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"143cd606fbca462b9faa79bab31836c1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4cb5a61cc0ce40bf8de08f03277c29b3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"25a7ae6d8e834e2a9c07da5a3d49d457"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"da7a342306094559b5aa460aee73ed89"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["normalizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f43ef7ee44d46dd814ac97e5a422ce7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["added_tokens.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c434d8fd380b4eb1b4a5a512a38d1744"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5ec7979296e84ed285b0de3f45d57ba8"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]},{"output_type":"display_data","data":{"text/plain":["preprocessor_config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e2a1d08a93a843fa9e2883c5d54d2138"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Due to a bug fix in https://github.com/huggingface/transformers/pull/28687 transcription using a multilingual Whisper will default to language detection followed by transcription instead of translation to English.This might be a breaking change for your use case. If you want to instead always translate your audio to English, make sure to pass `language='en'`.\n"]},{"output_type":"stream","name":"stdout","text":[" Hello, Chicago. It's good to be home. Tonight, it's my turn to say thanks. And every day I have learned from you. You made me a better president. And you made me a better man. I can't do that. I learned the change only happens when ordinary people get involved and they get engaged, and they come together to demand it. After eight years as your President, I still believe that. In ten days, the world will witness a hallmark of our democracy. No, no, no, no. The peaceful transfer of power from one freely elected President to the next. I committed to President-elect Trump that my administration would ensure the smoothest possible transition, just as President Bush did for me. After all, we remain the wealthiest, most powerful, and most respected nation on earth. Michelle, for the past 25 years, you have not only been my wife and mother of my children, you have been my best friend. You have made me proud, and you have made the country proud. I fell on Americans. It has been the honor of my life to serve you. I won't stop. In fact, I will be right there with you as a citizen for all my remaining days. I do have one final ask of you as your President. I am asking you to believe not in my ability to bring about change but in yours. Yes we can. Yes we did. Yes we can. Thank you. God bless them. May God continue to bless.\n"]}]},{"cell_type":"code","source":["!pip install edge-tts"],"metadata":{"collapsed":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"4FHZ9y8KUmnZ","executionInfo":{"status":"ok","timestamp":1779245952715,"user_tz":-540,"elapsed":5316,"user":{"displayName":"강사박진경","userId":"16101567623608115497"}},"outputId":"90f80b53-1aeb-4ce3-cad9-a5ccaf0c832c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting edge-tts\n","  Downloading edge_tts-7.2.8-py3-none-any.whl.metadata (5.5 kB)\n","Requirement already satisfied: aiohttp<4.0.0,>=3.8.0 in /usr/local/lib/python3.12/dist-packages (from edge-tts) (3.13.5)\n","Requirement already satisfied: certifi>=2023.11.17 in /usr/local/lib/python3.12/dist-packages (from edge-tts) (2026.4.22)\n","Requirement already satisfied: tabulate<1.0.0,>=0.4.4 in /usr/local/lib/python3.12/dist-packages (from edge-tts) (0.9.0)\n","Requirement already satisfied: typing-extensions<5.0.0,>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from edge-tts) (4.15.0)\n","Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (2.6.1)\n","Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (1.4.0)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (26.1.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (1.8.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (6.7.1)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (0.4.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp<4.0.0,>=3.8.0->edge-tts) (1.23.0)\n","Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.12/dist-packages (from yarl<2.0,>=1.17.0->aiohttp<4.0.0,>=3.8.0->edge-tts) (3.13)\n","Downloading edge_tts-7.2.8-py3-none-any.whl (31 kB)\n","Installing collected packages: edge-tts\n","Successfully installed edge-tts-7.2.8\n"]}]},{"cell_type":"code","source":["# 텍스트 => 음성\n","\n","import edge_tts\n","\n","text = \"안녕하세요 반갑습니다\"\n","voice = \"ko-KR-SunHiNeural\"\n","\n","output_file = \"/content/gdrive/MyDrive/Colab Notebooks/voice.mp3\"\n","\n","async def main():\n","  communicate = edge_tts.Communicate(text, voice)\n","  await communicate.save(output_file)\n","\n","await main()"],"metadata":{"id":"Jj0mLM4eTO5x"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/huggingface/notebooks/blob/master/course/ko/chapter1/section3.ipynb","timestamp":1779157783539}],"gpuType":"T4"},"language_info":{"name":"python"},"kernelspec":{"name":"python3","display_name":"Python 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\ No newline at end of file
diff --git a/pythonSource/Basic/exam1.ipynb b/pythonSource/Basic/exam1.ipynb
index 5f9411d..fadc573 100644
--- a/pythonSource/Basic/exam1.ipynb
+++ b/pythonSource/Basic/exam1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "327c097c",
    "metadata": {},
    "outputs": [
@@ -15,13 +15,16 @@
     }
    ],
    "source": [
+    "# 변수 : 프로그램 내부에서 값을 담아놓기 위한 공간(이름 사용)\n",
+    "# 정수형\n",
+    "\n",
     "a = 10\n",
     "print(a)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "4929f0bd",
    "metadata": {},
    "outputs": [
@@ -39,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "40c1b43b",
    "metadata": {},
    "outputs": [
@@ -67,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "c621c863",
    "metadata": {},
    "outputs": [
@@ -89,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "6af4a82e",
    "metadata": {},
    "outputs": [
@@ -115,34 +118,29 @@
     "print(a + b)\n",
     "print(a - b)\n",
     "print(a * b)\n",
-    "print(a / b)\n",
+    "print(a / b) # 몫과 나머지\n",
     "print(a // b) # 몫만 남김\n",
     "print(a % b) # 나머지 연산자, a를 b로 나눈 나머지를 구하는 연산자\n",
-    "print(a ** b)"
+    "print(a ** b) # a 의 b 제곱"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d6bd739",
+   "metadata": {},
+   "source": [
+    "### 문자열 자료형\n",
+    "- 홑따옴표('), 쌍따옴표(\") 사용\n",
+    "- 홑따옴표('''), 쌍따옴표(\"\"\") 사용 => multiline "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "97d1e1ad",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "unterminated string literal (detected at line 2) (3330389451.py, line 2)",
-     "output_type": "error",
-     "traceback": [
-      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31m- 홀따옴표 (') 또는 쌍따옴표 (\")로 감싸서 표현\u001b[39m\n            ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m unterminated string literal (detected at line 2)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "### 문자열 자료형\n",
-    "- 홀따옴표 (') 또는 쌍따옴표 (\")로 감싸서 표현\n",
-    "- 문자열 안에서 홀따옴표나 쌍따옴표를 사용\n",
-    "- 홀따옴표 (''') 또는 쌍따옴표 (\"\"\")로 감싸서 표현\n",
-    "                      \n",
-    "                      \n",
     "msg = 'Hello, World!'\n",
     "msg = \"Hello, World!\"\n",
     "msg = 'Hello, \"World!\"'\n",
@@ -155,7 +153,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
+   "id": "925a6c89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "food = \"Python's favorite food is perl\"\n",
+    "food"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "id": "9c73bd1b",
    "metadata": {},
    "outputs": [
@@ -165,7 +174,7 @@
        "'Life is too shortyou need python'"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -178,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "0900fed7",
    "metadata": {},
    "outputs": [
@@ -201,6 +210,14 @@
     "print(multiline)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e7228650",
+   "metadata": {},
+   "source": [
+    "# 문자열 연산"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -219,11 +236,11 @@
     }
    ],
    "source": [
-    "# 문자열 연산\n",
+    "# + : 연결\n",
+    "\n",
     "head = \"Python\"\n",
     "tail = \" is fun!\"\n",
-    "head + tail\n",
-    "\n"
+    "head + tail"
    ]
   },
   {
@@ -244,13 +261,13 @@
     }
    ],
    "source": [
-    "# 문자열 반복\n",
-    "head * 2 "
+    "# * : 반복\n",
+    "head * 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "id": "faad4d62",
    "metadata": {},
    "outputs": [
@@ -270,6 +287,18 @@
     "len(head)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "76aae57e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# len() : 길이 구하기\n",
+    "\n",
+    "len(head)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -301,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "id": "b34f4f42",
    "metadata": {},
    "outputs": [
@@ -326,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "id": "baa487b9",
    "metadata": {},
    "outputs": [
@@ -350,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "id": "c10a8379",
    "metadata": {},
    "outputs": [
@@ -388,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "id": "a2a40240",
    "metadata": {},
    "outputs": [
@@ -410,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "id": "09cdebd0",
    "metadata": {},
    "outputs": [
@@ -432,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "id": "2264a946",
    "metadata": {},
    "outputs": [
@@ -453,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "3fc4ce33",
    "metadata": {},
    "outputs": [
@@ -474,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "id": "04065ada",
    "metadata": {},
    "outputs": [
@@ -523,7 +552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": null,
    "id": "7114f4ab",
    "metadata": {},
    "outputs": [
@@ -560,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": null,
    "id": "795cb215",
    "metadata": {},
    "outputs": [],
@@ -576,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
    "id": "caf4c5ca",
    "metadata": {},
    "outputs": [
@@ -601,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": null,
    "id": "379aa915",
    "metadata": {},
    "outputs": [
@@ -630,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": null,
    "id": "7e58ae86",
    "metadata": {},
    "outputs": [
@@ -650,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": null,
    "id": "d5d35993",
    "metadata": {},
    "outputs": [
@@ -675,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": null,
    "id": "99d7f463",
    "metadata": {},
    "outputs": [
@@ -697,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": null,
    "id": "71e05f8d",
    "metadata": {},
    "outputs": [],
@@ -732,7 +761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": null,
    "id": "8d2c7520",
    "metadata": {},
    "outputs": [
@@ -777,7 +806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": null,
    "id": "30d312fe",
    "metadata": {},
    "outputs": [],
@@ -801,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": null,
    "id": "08f068a4",
    "metadata": {},
    "outputs": [
@@ -822,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": null,
    "id": "d80c64e6",
    "metadata": {},
    "outputs": [
@@ -843,7 +872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": null,
    "id": "8d47b622",
    "metadata": {},
    "outputs": [
@@ -898,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": null,
    "id": "3ecdc347",
    "metadata": {},
    "outputs": [],
@@ -914,7 +943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": null,
    "id": "028f2ca3",
    "metadata": {},
    "outputs": [
@@ -960,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": null,
    "id": "06ef8c0b",
    "metadata": {},
    "outputs": [
@@ -981,7 +1010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": null,
    "id": "1cbca157",
    "metadata": {},
    "outputs": [
@@ -1012,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": null,
    "id": "db7a6a5b",
    "metadata": {},
    "outputs": [],
@@ -1024,7 +1053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": null,
    "id": "cd658f9f",
    "metadata": {},
    "outputs": [
@@ -1047,7 +1076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": null,
    "id": "2abdb89f",
    "metadata": {},
    "outputs": [
@@ -1069,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": null,
    "id": "861fe8a2",
    "metadata": {},
    "outputs": [
@@ -1092,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": null,
    "id": "7376720f",
    "metadata": {},
    "outputs": [
@@ -1118,7 +1147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": null,
    "id": "8fd35f51",
    "metadata": {},
    "outputs": [
@@ -1141,7 +1170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": null,
    "id": "ef7f8e27",
    "metadata": {},
    "outputs": [
@@ -1205,7 +1234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": null,
    "id": "ad742983",
    "metadata": {},
    "outputs": [
@@ -1229,7 +1258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": null,
    "id": "58f6afd8",
    "metadata": {},
    "outputs": [
@@ -1307,7 +1336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": null,
    "id": "af6f7357",
    "metadata": {},
    "outputs": [
@@ -1335,7 +1364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": null,
    "id": "ff6d1883",
    "metadata": {},
    "outputs": [
@@ -1367,7 +1396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": null,
    "id": "a18e0c62",
    "metadata": {},
    "outputs": [
@@ -1386,7 +1415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": null,
    "id": "c6c03f4e",
    "metadata": {},
    "outputs": [
@@ -1407,7 +1436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": null,
    "id": "d3e98df6",
    "metadata": {},
    "outputs": [
@@ -1437,7 +1466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": null,
    "id": "414aa9d0",
    "metadata": {},
    "outputs": [
@@ -1461,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": null,
    "id": "746a8f64",
    "metadata": {},
    "outputs": [
@@ -1480,13 +1509,4049 @@
     "print(msg)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "637f3673",
+   "metadata": {},
+   "source": [
+    "- while 문\n",
+    "\n",
+    "while 조건:\n",
+    "    반복해서 해야 할 일들\n",
+    "\n",
+    "- for 문\n",
+    "\n",
+    "for 변수 in 리스트 (또는 튜플, 문자열, 딕셔너리..):\n",
+    "    반복해서 해야할 일들"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "b7c22708",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 리스트 출력\n",
+    "list1 = [1, 2, 3, 4, 5]\n",
+    "print(list1)\n",
+    "\n",
+    "for i in list1:\n",
+    "    print(i)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5f8a7f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "t1 = (1, 2, 3)\n",
+    "for i in t1:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78a0326b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "7\n",
+      "11\n"
+     ]
+    }
+   ],
+   "source": [
+    "list1 = [(1, 2), (3, 4), (5, 6)]\n",
+    "for first, last in list1:\n",
+    "    print(first + last)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f01cfe76",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\t2\t3\t4\t5\t6\t7\t8\t9\t10\t"
+     ]
+    }
+   ],
+   "source": [
+    "# 1 ~ 10 까지 cnffur\n",
+    "i = 1\n",
+    "\n",
+    "while i <= 10:\n",
+    "    print(i, end='\\t')\n",
+    "    i += 1\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95be308e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "총합 : 790, 평균 : 79.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 총 10명의 학생 점수\n",
+    "classA = [70, 60, 55, 75, 95, 90, 80, 80, 85, 100]\n",
+    "\n",
+    "# 총합과 평균 구하기\n",
+    "\n",
+    "total = 0\n",
+    "for score in classA:\n",
+    "    total += score\n",
+    "    \n",
+    "avg = total / len(classA)\n",
+    "\n",
+    "print(f\"총합 : {total}, 평균 : {avg}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb7b38c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "79.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 평균\n",
+    "sum(classA) / len(classA)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e77a0e61",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range(1, 10, 2)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "range(5) # 0 ~ 4까지의 정수 생성\n",
+    "range(1, 6) # 1 ~ 5까지의 정수 생성\n",
+    "range(1, 10, 2) # 1 ~ 9까지의 정수 생성, 2씩 증가\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4107acf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "1\n",
+      "3\n",
+      "5\n",
+      "7\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "for i in range(5):\n",
+    "    print(i)\n",
+    "    \n",
+    "    \n",
+    "for i in range(1, 11):\n",
+    "    print(i)\n",
+    "\n",
+    "for i in range(1, 11, 2):\n",
+    "    print(i)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a50256f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dreams\n",
+      "d\n",
+      "d\n",
+      "r\n",
+      "e\n",
+      "a\n",
+      "m\n",
+      "s\n"
+     ]
+    }
+   ],
+   "source": [
+    "msg = \"dreams\"\n",
+    "print(msg)\n",
+    "\n",
+    "print(msg[0])\n",
+    "\n",
+    "for s in msg:\n",
+    "    print(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a8afbe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'a': 1, 'b': 80, 'c': 70}\n",
+      "a 1\n",
+      "b 80\n",
+      "c 70\n"
+     ]
+    }
+   ],
+   "source": [
+    "dict1 = { 'a': 1, 'b': 80, 'c': 70 }\n",
+    "print(dict1)\n",
+    "\n",
+    "for key, value in dict1.items():\n",
+    "    print(key, value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32f4103f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1,\n",
+       " 2,\n",
+       " 3,\n",
+       " 4,\n",
+       " 5,\n",
+       " 6,\n",
+       " 7,\n",
+       " 8,\n",
+       " 9,\n",
+       " 10,\n",
+       " 11,\n",
+       " 12,\n",
+       " 13,\n",
+       " 14,\n",
+       " 15,\n",
+       " 16,\n",
+       " 17,\n",
+       " 18,\n",
+       " 19,\n",
+       " 20,\n",
+       " 21,\n",
+       " 22,\n",
+       " 23,\n",
+       " 24,\n",
+       " 25,\n",
+       " 26,\n",
+       " 27,\n",
+       " 28,\n",
+       " 29,\n",
+       " 30,\n",
+       " 31,\n",
+       " 32,\n",
+       " 33,\n",
+       " 34,\n",
+       " 35,\n",
+       " 36,\n",
+       " 37,\n",
+       " 38,\n",
+       " 39,\n",
+       " 40,\n",
+       " 41,\n",
+       " 42,\n",
+       " 43,\n",
+       " 44,\n",
+       " 45,\n",
+       " 46,\n",
+       " 47,\n",
+       " 48,\n",
+       " 49,\n",
+       " 50,\n",
+       " 51,\n",
+       " 52,\n",
+       " 53,\n",
+       " 54,\n",
+       " 55,\n",
+       " 56,\n",
+       " 57,\n",
+       " 58,\n",
+       " 59,\n",
+       " 60,\n",
+       " 61,\n",
+       " 62,\n",
+       " 63,\n",
+       " 64,\n",
+       " 65,\n",
+       " 66,\n",
+       " 67,\n",
+       " 68,\n",
+       " 69,\n",
+       " 70,\n",
+       " 71,\n",
+       " 72,\n",
+       " 73,\n",
+       " 74,\n",
+       " 75,\n",
+       " 76,\n",
+       " 77,\n",
+       " 78,\n",
+       " 79,\n",
+       " 80,\n",
+       " 81,\n",
+       " 82,\n",
+       " 83,\n",
+       " 84,\n",
+       " 85,\n",
+       " 86,\n",
+       " 87,\n",
+       " 88,\n",
+       " 89,\n",
+       " 90,\n",
+       " 91,\n",
+       " 92,\n",
+       " 93,\n",
+       " 94,\n",
+       " 95,\n",
+       " 96,\n",
+       " 97,\n",
+       " 98,\n",
+       " 99,\n",
+       " 100]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(range(1, 101))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fda43c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1~10까지 리스트로 생성\n",
+    "numbers = []\n",
+    "for i in range(1, 11):\n",
+    "    numbers.append(i)\n",
+    "\n",
+    "print(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17b64a5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['b', 'c', 'd']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 리스트 컴프리헨션\n",
+    "\n",
+    "list2 = [\"A\", \"b\", \"c\", \"E\", \"Q\", \"d\"]\n",
+    "\n",
+    "# 소문자만 골라서 새로운 리스트 생성\n",
+    "# lowers = []\n",
+    "# for s in list2:\n",
+    "#     if s.islower():\n",
+    "#         lowers.append(s)\n",
+    "\n",
+    "# print(lowers)\n",
+    "\n",
+    "lowers = [s for s in list2 if s.islower()]\n",
+    "print(lowers)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fb9fb21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3, 6, 9, 12, 15]\n"
+     ]
+    }
+   ],
+   "source": [
+    "nums = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "# nums의 각 요소에 * 3 한 결과 results 새로운 리스트 생성\n",
+    "results = [n * 3 for n in nums]\n",
+    "print(results)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec857421",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[6, 12]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# nums의 각 요소에 짝수에 * 3 한 결과 results 새로운 리스트 생성\n",
+    "results = [n * 3 for n in nums if n % 2 == 0]\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "2794cdae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 리스트\n",
+    "# 반복문, 조건문\n",
+    "\n",
+    "ord(\"A\") # 65\n",
+    "chr(65) # \"A\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "0b0e8214",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A 자동차가 주차되었습니다. 주차장 상태 : ['A']\n",
+      "주차장이 비어 있습니다.\n",
+      "A 자동차가 주차되었습니다. 주차장 상태 : ['A']\n",
+      "주차장이 비어 있습니다.\n",
+      "A 자동차가 주차되었습니다. 주차장 상태 : ['A']\n",
+      "B 자동차가 주차되었습니다. 주차장 상태 : ['A', 'B']\n",
+      "C 자동차가 주차되었습니다. 주차장 상태 : ['A', 'B', 'C']\n",
+      "C 자동차가 출차되었습니다. 주차장 상태 : ['A', 'B']\n",
+      "B 자동차가 출차되었습니다. 주차장 상태 : ['A']\n",
+      "주차장이 비어 있습니다.\n",
+      "프로그램을 종료합니다.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 주차장 프로그램?\n",
+    "# 5대의 주차 공간, a, b,c, d, e\n",
+    "\n",
+    "# 메뉴 제공\n",
+    "# [1] 주차 [2] 출차 [3] 종료\n",
+    "\n",
+    "# 1번 선택 => a 자동차 들어감. 주차장 상태 => ['a']\n",
+    "# 1번 선택 => b 자동차 들어감. 주차장 상태 => ['a', 'b']\n",
+    "# 2번 선택 => b 자동차 나감. 주차장 상태 => ['a']\n",
+    "\n",
+    "# 5대의 주차 공간이 모두 찼을 때 1번 선택 => \"주차 공간이 없습니다.\"\n",
+    "\n",
+    "parking_lot = []\n",
+    "\n",
+    "while True:\n",
+    "    menu = input(\"[1] 주차 | [2] 출차 | [3] 종료 : 숫자만 입력하시오 = \")\n",
+    "    \n",
+    "    if menu == \"1\":\n",
+    "        if len(parking_lot) < 5:\n",
+    "            car = len(parking_lot) + 65 # \"A\"\n",
+    "            parking_lot.append(car)\n",
+    "            print(f\"{chr(car)} 자동차가 주차되었습니다. 주차장 상태 : {[chr(c) for c in parking_lot]}\")\n",
+    "        else:\n",
+    "            print(\"주차 공간이 없습니다.\")\n",
+    "            \n",
+    "    elif menu == \"2\":\n",
+    "        if parking_lot:\n",
+    "            car = parking_lot.pop()\n",
+    "            \n",
+    "            if not parking_lot:\n",
+    "                print(\"주차장이 비어 있습니다.\")\n",
+    "            else:\n",
+    "                print(f\"{chr(car)} 자동차가 출차되었습니다. 주차장 상태 : {[chr(c) for c in parking_lot]}\")\n",
+    "        else:\n",
+    "            print(\"주차장이 비어 있습니다.\")\n",
+    "            \n",
+    "    elif menu == \"3\":\n",
+    "        print(\"프로그램을 종료합니다.\")\n",
+    "        break\n",
+    "        \n",
+    "    else:\n",
+    "        print(\"잘못된 선택입니다. 다시 선택해주세요.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f59c8442",
+   "metadata": {},
+   "source": [
+    "### 함수\n",
+    " \n",
+    "```python\n",
+    "def 함수명() :\n",
+    "    함수에서 해야할 일들\n",
+    "```\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "047354a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n"
+     ]
+    }
+   ],
+   "source": [
+    "#range()\n",
+    "#int()\n",
+    "#input()\n",
+    "#len()\n",
+    "#str()\n",
+    "#list()\n",
+    "#tuple()\n",
+    "#dict()\n",
+    "#set()\n",
+    "#type()\n",
+    "#print()\n",
+    "\n",
+    "# 함수 정의\n",
+    "def add(a, b):\n",
+    "    return a + b\n",
+    "\n",
+    "results = add(10, 20)\n",
+    "print(results)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "9941c444",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(add(5, 7))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "51e1a73e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n",
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add(a, b = 5):\n",
+    "    return a + b\n",
+    "\n",
+    "print(add(5, 7))\n",
+    "print(add(5))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "fc28339e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n",
+      "50\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add_many(*args):\n",
+    "    total = 0\n",
+    "    for num in args:\n",
+    "        total += num\n",
+    "    return total\n",
+    "\n",
+    "print(add_many(1, 2, 3, 4, 5))\n",
+    "print(add_many(1, 19, 30))\n",
+    "print(add_many())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "9071a280",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n",
+      "120\n"
+     ]
+    }
+   ],
+   "source": [
+    "def add_many(choice, *args):\n",
+    "\n",
+    "    if choice == \"mul\":\n",
+    "        result = 1\n",
+    "        for num in args:\n",
+    "            result *= num\n",
+    "    elif choice == \"add\":\n",
+    "        result = 0\n",
+    "        for num in args:\n",
+    "            result += num\n",
+    "    return result\n",
+    "\n",
+    "print(add_many(\"add\", 1, 2, 3, 4, 5))\n",
+    "print(add_many(\"mul\", 1, 2, 3, 4, 5))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "30c1ea8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a : 1\n",
+      "b : 2\n",
+      "c : 3\n",
+      "d : 4\n",
+      "e : 5\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ** kwargs : 키워드 인수, 함수에 전달되는 인수 중에서 키워드 인수는 함수 정의에서 매개변수 이름과 함께 값을 전달하는 방식입니다. 키워드 인수를 사용하면 함수 호출 시 매개변수의 순서에 상관없이 값을 전달할 수 있습니다. 예를 들어, def func(a, b): ...와 같이 함수가 정의되어 있을 때, func(b=2, a=1)와 같이 키워드 인수를 사용하여 값을 전달할 수 있습니다. 키워드 인수는 함수 호출 시 가독성을 높이고, 매개변수의 순서를 신경쓰지 않아도 되는 장점이 있습니다.\n",
+    "# *args : 가변 인수, 함수에 전달되는 인수 중에서 가변 인수는 함수 정의에서 매개변수 이름 앞에 *를 붙여서 정의하는 방식입니다. 가변 인수를 사용하면 함수 호출 시 전달되는 인수의 개수가 가변적일 수 있습니다. 예를 들어, def func(*args): ...와 같이 함수가 정의되어 있을 때, func(1, 2, 3)와 같이 여러 개의 인수를 전달할 수 있습니다. 가변 인수는 함수 호출 시 다양한 개수의 인수를 처리할 때 유용하게 사용됩니다.\n",
+    "# 변수명 : 1:1 대응\n",
+    "\n",
+    "def add_many(**kwargs):\n",
+    "    for k, v in kwargs.items():\n",
+    "        print(f\"{k} : {v}\")\n",
+    "\n",
+    "print(add_many(a=1, b=2, c=3, d=4, e=5))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "4662bc09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 5 () {}\n",
+      "1 2 () {}\n",
+      "1 2 (3, 4, 5) {}\n",
+      "1 2 () {'a': 6, 'b': 7}\n",
+      "1 2 (3, 4, 5) {'a': 6, 'b': 7}\n"
+     ]
+    }
+   ],
+   "source": [
+    "def example_mul(arg1, arg2=5, *args, **kwargs):\n",
+    "    print(arg1, arg2, args, kwargs)\n",
+    "    \n",
+    "# example_mul()\n",
+    "example_mul(10)\n",
+    "example_mul(1, 2)\n",
+    "example_mul(1, 2, 3, 4, 5)\n",
+    "example_mul(1, 2, a=6, b=7)\n",
+    "example_mul(1, 2, 3, 4, 5, a=6, b=7)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "db75df55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(7, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def sum_add(a, b):\n",
+    "    return a + b, a * b\n",
+    "\n",
+    "print(sum_add(3, 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9ea151c",
+   "metadata": {},
+   "source": [
+    "### lamda 함수\n",
+    "- 단일문으로 표현되는 익명 함수\n",
+    "- 1회용 함수로 주로 사용"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ddf88de2",
+   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "### lamda 함수\n",
+    "- 단일문으로 표현되는 익명 함수\n",
+    "- 1회용 함수로 주로 사용"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "de8dbeda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "25\n",
+      "25\n"
+     ]
+    }
+   ],
+   "source": [
+    "def square(x):\n",
+    "    return x ** 2\n",
+    "\n",
+    "print(square(5))\n",
+    "\n",
+    "# lambda 함수로 표현\n",
+    "square_lambda = lambda x: x ** 2\n",
+    "print(square_lambda(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "9f63beb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "#def add(a, b=5):\n",
+    "#    return a + b\n",
+    "\n",
+    "lambda_add = lambda a, b: a + b\n",
+    "print(lambda_add(3, 4))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "1a999f77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['bob', 'alice', 'teddy', 'charles']\n"
+     ]
+    }
+   ],
+   "source": [
+    "string = [\"bob\", \"charles\", \"alice\", \"teddy\"]\n",
+    "\n",
+    "#sort() : 내림 오름\n",
+    "string.sort()\n",
+    "\n",
+    "#sort(key = 함수) : 문자길이 정렬\n",
+    "\n",
+    "# def str_len(s):\n",
+    "#     return len(s)\n",
+    "\n",
+    "# string.sort(key=str_len)\n",
+    "# string\n",
+    "string.sort(key=lambda s: len(s))\n",
+    "print(string)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "19157dae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 4, 6, 8, 10]\n",
+      "[2, 4, 6, 8, 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# filter, map\n",
+    "\n",
+    "# 넘겨받은 리스트에서 짝수만을 추출후 새로운 리스트로 리턴\n",
+    "\n",
+    "def even(arr):\n",
+    "    even_list = []\n",
+    "    for n in arr:\n",
+    "        if n % 2 == 0:\n",
+    "            even_list.append(n)\n",
+    "    return even_list\n",
+    "\n",
+    "list1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+    "even_list = even(list1)\n",
+    "print(even_list)\n",
+    "\n",
+    "def is_even(n):\n",
+    "    return n % 2 == 0\n",
+    "\n",
+    "numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+    "even_numbers = filter(is_even, numbers)\n",
+    "print(list(even_numbers))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "4fdbc48c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2, 4, 6, 8, 10]"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(filter(lambda n: n % 2 == 0, numbers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "b4da5d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[False, True, False, True, False, True, False, True, False, True]"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(map(lambda n: n %2==0, numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "881aeb4d",
+   "metadata": {},
+   "source": [
+    "# 파일 입출력\n",
+    "\n",
+    "``` open(파일이름, mode) ```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "eb3148ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 파일 읽기\n",
+    "f1 = open(\"test.txt\", \"r\") # 파일을 읽기 모드로 엽니다. open() 함수는 파일을 열 때 사용되며, 첫 번째 매개변수로 파일 경로를, 두 번째 매개변수로 파일 모드를 지정합니다. \"r\" 모드는 읽기 모드로 파일을 열며, 파일이 존재하지 않으면 FileNotFoundError 예외를 발생시킵니다. open() 함수는 파일 객체를 반환하며, 이후에 파일 작업을 수행할 수 있습니다.     \n",
+    "# 파일 쓰기\n",
+    "f2 = open(\"test.txt\", \"w\") # 파일을 쓰기 모드로 엽니다\n",
+    "# 파일에 내용 추가  \n",
+    "f3 = open(\"test.txt\", \"a\") # 파일을 추가 모드로 엽니다. open() 함수는 파일을 열 때 사용되며, 첫 번째 매개변수로 파일 경로를, 두 번째 매개변수로 파일 모드를 지정합니다. \"a\" 모드는 추가 모드로 파일을 열며, 파일이 존재하지 않으면 새로 생성합니다. open() 함수는 파일 객체를 반환하며, 이후에 파일 작업을 수행할 수 있습니다. 추가 모드는 기존 파일의 내용을 유지하면서 새로운 내용을 추가할 때 유용하게 사용됩니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "f58cb422",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test\n"
+     ]
+    }
+   ],
+   "source": [
+    "f1 = open(\"test.txt\", \"r\", encoding=\"utf-8\") # 파일을 읽기 모드로 열면서 인코딩을 utf-8로 지정합니다. open() 함수는 파일을 열 때 사용되며, 첫 번째 매개변수로 파일 경로를, 두 번째 매개변수로 파일 모드를 지정합니다. \"r\" 모드는 읽기 모드로 파일을 열며, encoding=\"utf-8\"은 파일의 인코딩을 utf-8로 지정하는 옵션입니다. open() 함수는 파일 객체를 반환하며, 이후에 파일 작업을 수행할 수 있습니다. 인코딩을 지정하면 파일의 내용을 올바르게 읽어들일 수 있습니다.\n",
+    "print(f1.read()) # 파일의 전체 내용을 읽어서 문자열로 반환하는 함수입니다. read() 함수는 파일 객체에서 전체 내용을 읽어들이며, 반환값은 문자열입니다. 예를 들어, f1.read()는 f1 파일 객체에서 전체 내용을 읽어서 문자열로 반환합니다. read() 함수는 파일의 내용을 한 번에 읽어들일 때 유용하게 사용됩니다.  \n",
+    "f1.close() # 파일을 닫습니다. close() 함수는 파일 작업이 끝난 후에 파일을 닫을 때 사용됩니다. 파일을 열 때 open() 함수를 사용하여 파일 객체를 생성한 후, 작업이 완료되면 close() 함수를 호출하여 파일을 닫아야 합니다. close() 함수를 호출하지 않으면 파일이 계속 열려 있을 수 있으며, 이는 시스템 자원을 낭비할 수 있습니다. 따라서 파일 작업이 끝난 후에는 반드시 close() 함수를 호출하여 파일을 닫는 것이 좋습니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "9958ad37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"test.txt\", \"r\", encoding=\"utf-8\") as f1:\n",
+    "    print(f1.read()) # with 문을 사용하여 파일을 열면, 블록이 끝날 때 자동으로 파일이 닫힙니다. 따라서 with open(\"test.txt\", \"r\", encoding=\"utf-8\") as f1:와 같이 파일을 열고, 블록 내에서 f1.read()를 호출하여 파일의 내용을 읽어들인 후, 블록이 끝나면 자동으로 f1 파일 객체가 닫히게 됩니다. with 문은 파일 작업을 안전하게 처리할 수 있도록 도와주며, 파일을 열고 닫는 것을 자동으로 관리해줍니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "55b73588",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"test2.txt\", \"w\", encoding=\"utf-8\") as f1:\n",
+    "    for i in range(3):\n",
+    "        f1.write(f\"Line {i+1}\\n\") # 파일 객체 f1에 문자열을 쓰는 함수입니다. write() 함수는 파일 객체에 문자열을 쓰며, 반환값은 쓰여진 문자열의 길이입니다. 예를 들어, f1.write(f\"Line {i+1}\\n\")는 f1 파일 객체에 \"Line 1\\n\", \"Line 2\\n\", \"Line 3\\n\"과 같은 문자열을 순차적으로 쓰게 됩니다. write() 함수는 파일에 데이터를 기록할 때 유용하게 사용됩니다.  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "3b92d329",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "hanguls = list(\"가나다라마바사아자차카타파하\")\n",
+    "with open(\"info.txt\", \"w\", encoding=\"utf-8\") as f1:\n",
+    "    for i in range(500):\n",
+    "        name = random.choice(hanguls) + random.choice(hanguls)\n",
+    "        weight = random.randint(40, 100)\n",
+    "        height = random.randint(140, 200)\n",
+    "        f1.write(f\"{name} {weight} {height}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "8753e450",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "파다 98 170\n",
+      "\n",
+      " 이름 : 파다, 몸무게 : 98kg, 키 : 170cm, bmi 지수 : 33.91, 결과 : 과체중\n",
+      "\n",
+      "차카 63 194\n",
+      "\n",
+      " 이름 : 차카, 몸무게 : 63kg, 키 : 194cm, bmi 지수 : 16.74, 결과 : 저체중\n",
+      "\n",
+      "바카 42 152\n",
+      "\n",
+      " 이름 : 바카, 몸무게 : 42kg, 키 : 152cm, bmi 지수 : 18.18, 결과 : 저체중\n",
+      "\n",
+      "마파 62 157\n",
+      "\n",
+      " 이름 : 마파, 몸무게 : 62kg, 키 : 157cm, bmi 지수 : 25.15, 결과 : 과체중\n",
+      "\n",
+      "라마 77 156\n",
+      "\n",
+      " 이름 : 라마, 몸무게 : 77kg, 키 : 156cm, bmi 지수 : 31.64, 결과 : 과체중\n",
+      "\n",
+      "카다 40 143\n",
+      "\n",
+      " 이름 : 카다, 몸무게 : 40kg, 키 : 143cm, bmi 지수 : 19.56, 결과 : 정상\n",
+      "\n",
+      "가하 63 150\n",
+      "\n",
+      " 이름 : 가하, 몸무게 : 63kg, 키 : 150cm, bmi 지수 : 28.00, 결과 : 과체중\n",
+      "\n",
+      "라가 78 153\n",
+      "\n",
+      " 이름 : 라가, 몸무게 : 78kg, 키 : 153cm, bmi 지수 : 33.32, 결과 : 과체중\n",
+      "\n",
+      "라자 88 189\n",
+      "\n",
+      " 이름 : 라자, 몸무게 : 88kg, 키 : 189cm, bmi 지수 : 24.64, 결과 : 정상\n",
+      "\n",
+      "파다 47 186\n",
+      "\n",
+      " 이름 : 파다, 몸무게 : 47kg, 키 : 186cm, bmi 지수 : 13.59, 결과 : 저체중\n",
+      "\n",
+      "파가 50 168\n",
+      "\n",
+      " 이름 : 파가, 몸무게 : 50kg, 키 : 168cm, bmi 지수 : 17.72, 결과 : 저체중\n",
+      "\n",
+      "바타 59 194\n",
+      "\n",
+      " 이름 : 바타, 몸무게 : 59kg, 키 : 194cm, bmi 지수 : 15.68, 결과 : 저체중\n",
+      "\n",
+      "파사 63 196\n",
+      "\n",
+      " 이름 : 파사, 몸무게 : 63kg, 키 : 196cm, bmi 지수 : 16.40, 결과 : 저체중\n",
+      "\n",
+      "하차 87 195\n",
+      "\n",
+      " 이름 : 하차, 몸무게 : 87kg, 키 : 195cm, bmi 지수 : 22.88, 결과 : 정상\n",
+      "\n",
+      "마파 65 152\n",
+      "\n",
+      " 이름 : 마파, 몸무게 : 65kg, 키 : 152cm, bmi 지수 : 28.13, 결과 : 과체중\n",
+      "\n",
+      "하파 62 145\n",
+      "\n",
+      " 이름 : 하파, 몸무게 : 62kg, 키 : 145cm, bmi 지수 : 29.49, 결과 : 과체중\n",
+      "\n",
+      "차바 69 159\n",
+      "\n",
+      " 이름 : 차바, 몸무게 : 69kg, 키 : 159cm, bmi 지수 : 27.29, 결과 : 과체중\n",
+      "\n",
+      "카차 70 196\n",
+      "\n",
+      " 이름 : 카차, 몸무게 : 70kg, 키 : 196cm, bmi 지수 : 18.22, 결과 : 저체중\n",
+      "\n",
+      "바라 48 147\n",
+      "\n",
+      " 이름 : 바라, 몸무게 : 48kg, 키 : 147cm, bmi 지수 : 22.21, 결과 : 정상\n",
+      "\n",
+      "자나 43 146\n",
+      "\n",
+      " 이름 : 자나, 몸무게 : 43kg, 키 : 146cm, bmi 지수 : 20.17, 결과 : 정상\n",
+      "\n",
+      "파마 88 149\n",
+      "\n",
+      " 이름 : 파마, 몸무게 : 88kg, 키 : 149cm, bmi 지수 : 39.64, 결과 : 과체중\n",
+      "\n",
+      "나나 67 155\n",
+      "\n",
+      " 이름 : 나나, 몸무게 : 67kg, 키 : 155cm, bmi 지수 : 27.89, 결과 : 과체중\n",
+      "\n",
+      "파가 49 152\n",
+      "\n",
+      " 이름 : 파가, 몸무게 : 49kg, 키 : 152cm, bmi 지수 : 21.21, 결과 : 정상\n",
+      "\n",
+      "자차 96 140\n",
+      "\n",
+      " 이름 : 자차, 몸무게 : 96kg, 키 : 140cm, bmi 지수 : 48.98, 결과 : 과체중\n",
+      "\n",
+      "사마 88 184\n",
+      "\n",
+      " 이름 : 사마, 몸무게 : 88kg, 키 : 184cm, bmi 지수 : 25.99, 결과 : 과체중\n",
+      "\n",
+      "사바 90 174\n",
+      "\n",
+      " 이름 : 사바, 몸무게 : 90kg, 키 : 174cm, bmi 지수 : 29.73, 결과 : 과체중\n",
+      "\n",
+      "나라 58 167\n",
+      "\n",
+      " 이름 : 나라, 몸무게 : 58kg, 키 : 167cm, bmi 지수 : 20.80, 결과 : 정상\n",
+      "\n",
+      "파나 66 152\n",
+      "\n",
+      " 이름 : 파나, 몸무게 : 66kg, 키 : 152cm, bmi 지수 : 28.57, 결과 : 과체중\n",
+      "\n",
+      "타나 86 170\n",
+      "\n",
+      " 이름 : 타나, 몸무게 : 86kg, 키 : 170cm, bmi 지수 : 29.76, 결과 : 과체중\n",
+      "\n",
+      "아나 93 195\n",
+      "\n",
+      " 이름 : 아나, 몸무게 : 93kg, 키 : 195cm, bmi 지수 : 24.46, 결과 : 정상\n",
+      "\n",
+      "사하 61 151\n",
+      "\n",
+      " 이름 : 사하, 몸무게 : 61kg, 키 : 151cm, bmi 지수 : 26.75, 결과 : 과체중\n",
+      "\n",
+      "타하 85 142\n",
+      "\n",
+      " 이름 : 타하, 몸무게 : 85kg, 키 : 142cm, bmi 지수 : 42.15, 결과 : 과체중\n",
+      "\n",
+      "카차 73 140\n",
+      "\n",
+      " 이름 : 카차, 몸무게 : 73kg, 키 : 140cm, bmi 지수 : 37.24, 결과 : 과체중\n",
+      "\n",
+      "카마 90 157\n",
+      "\n",
+      " 이름 : 카마, 몸무게 : 90kg, 키 : 157cm, bmi 지수 : 36.51, 결과 : 과체중\n",
+      "\n",
+      "가가 41 180\n",
+      "\n",
+      " 이름 : 가가, 몸무게 : 41kg, 키 : 180cm, bmi 지수 : 12.65, 결과 : 저체중\n",
+      "\n",
+      "나나 56 198\n",
+      "\n",
+      " 이름 : 나나, 몸무게 : 56kg, 키 : 198cm, bmi 지수 : 14.28, 결과 : 저체중\n",
+      "\n",
+      "다파 40 161\n",
+      "\n",
+      " 이름 : 다파, 몸무게 : 40kg, 키 : 161cm, bmi 지수 : 15.43, 결과 : 저체중\n",
+      "\n",
+      "나카 62 170\n",
+      "\n",
+      " 이름 : 나카, 몸무게 : 62kg, 키 : 170cm, bmi 지수 : 21.45, 결과 : 정상\n",
+      "\n",
+      "차다 88 187\n",
+      "\n",
+      " 이름 : 차다, 몸무게 : 88kg, 키 : 187cm, bmi 지수 : 25.17, 결과 : 과체중\n",
+      "\n",
+      "가자 45 150\n",
+      "\n",
+      " 이름 : 가자, 몸무게 : 45kg, 키 : 150cm, bmi 지수 : 20.00, 결과 : 정상\n",
+      "\n",
+      "사다 80 195\n",
+      "\n",
+      " 이름 : 사다, 몸무게 : 80kg, 키 : 195cm, bmi 지수 : 21.04, 결과 : 정상\n",
+      "\n",
+      "카바 84 192\n",
+      "\n",
+      " 이름 : 카바, 몸무게 : 84kg, 키 : 192cm, bmi 지수 : 22.79, 결과 : 정상\n",
+      "\n",
+      "하자 98 164\n",
+      "\n",
+      " 이름 : 하자, 몸무게 : 98kg, 키 : 164cm, bmi 지수 : 36.44, 결과 : 과체중\n",
+      "\n",
+      "카자 92 177\n",
+      "\n",
+      " 이름 : 카자, 몸무게 : 92kg, 키 : 177cm, bmi 지수 : 29.37, 결과 : 과체중\n",
+      "\n",
+      "다다 41 150\n",
+      "\n",
+      " 이름 : 다다, 몸무게 : 41kg, 키 : 150cm, bmi 지수 : 18.22, 결과 : 저체중\n",
+      "\n",
+      "아타 86 170\n",
+      "\n",
+      " 이름 : 아타, 몸무게 : 86kg, 키 : 170cm, bmi 지수 : 29.76, 결과 : 과체중\n",
+      "\n",
+      "마카 50 182\n",
+      "\n",
+      " 이름 : 마카, 몸무게 : 50kg, 키 : 182cm, bmi 지수 : 15.09, 결과 : 저체중\n",
+      "\n",
+      "마자 72 175\n",
+      "\n",
+      " 이름 : 마자, 몸무게 : 72kg, 키 : 175cm, bmi 지수 : 23.51, 결과 : 정상\n",
+      "\n",
+      "사다 40 158\n",
+      "\n",
+      " 이름 : 사다, 몸무게 : 40kg, 키 : 158cm, bmi 지수 : 16.02, 결과 : 저체중\n",
+      "\n",
+      "사아 93 174\n",
+      "\n",
+      " 이름 : 사아, 몸무게 : 93kg, 키 : 174cm, bmi 지수 : 30.72, 결과 : 과체중\n",
+      "\n",
+      "가라 58 153\n",
+      "\n",
+      " 이름 : 가라, 몸무게 : 58kg, 키 : 153cm, bmi 지수 : 24.78, 결과 : 정상\n",
+      "\n",
+      "자아 77 196\n",
+      "\n",
+      " 이름 : 자아, 몸무게 : 77kg, 키 : 196cm, bmi 지수 : 20.04, 결과 : 정상\n",
+      "\n",
+      "아바 42 162\n",
+      "\n",
+      " 이름 : 아바, 몸무게 : 42kg, 키 : 162cm, bmi 지수 : 16.00, 결과 : 저체중\n",
+      "\n",
+      "마하 56 175\n",
+      "\n",
+      " 이름 : 마하, 몸무게 : 56kg, 키 : 175cm, bmi 지수 : 18.29, 결과 : 저체중\n",
+      "\n",
+      "파파 93 187\n",
+      "\n",
+      " 이름 : 파파, 몸무게 : 93kg, 키 : 187cm, bmi 지수 : 26.59, 결과 : 과체중\n",
+      "\n",
+      "파마 91 172\n",
+      "\n",
+      " 이름 : 파마, 몸무게 : 91kg, 키 : 172cm, bmi 지수 : 30.76, 결과 : 과체중\n",
+      "\n",
+      "카다 80 146\n",
+      "\n",
+      " 이름 : 카다, 몸무게 : 80kg, 키 : 146cm, bmi 지수 : 37.53, 결과 : 과체중\n",
+      "\n",
+      "가다 95 166\n",
+      "\n",
+      " 이름 : 가다, 몸무게 : 95kg, 키 : 166cm, bmi 지수 : 34.48, 결과 : 과체중\n",
+      "\n",
+      "카카 42 150\n",
+      "\n",
+      " 이름 : 카카, 몸무게 : 42kg, 키 : 150cm, bmi 지수 : 18.67, 결과 : 정상\n",
+      "\n",
+      "라차 50 191\n",
+      "\n",
+      " 이름 : 라차, 몸무게 : 50kg, 키 : 191cm, bmi 지수 : 13.71, 결과 : 저체중\n",
+      "\n",
+      "아바 95 143\n",
+      "\n",
+      " 이름 : 아바, 몸무게 : 95kg, 키 : 143cm, bmi 지수 : 46.46, 결과 : 과체중\n",
+      "\n",
+      "바마 91 175\n",
+      "\n",
+      " 이름 : 바마, 몸무게 : 91kg, 키 : 175cm, bmi 지수 : 29.71, 결과 : 과체중\n",
+      "\n",
+      "파라 85 164\n",
+      "\n",
+      " 이름 : 파라, 몸무게 : 85kg, 키 : 164cm, bmi 지수 : 31.60, 결과 : 과체중\n",
+      "\n",
+      "마라 85 168\n",
+      "\n",
+      " 이름 : 마라, 몸무게 : 85kg, 키 : 168cm, bmi 지수 : 30.12, 결과 : 과체중\n",
+      "\n",
+      "자하 89 192\n",
+      "\n",
+      " 이름 : 자하, 몸무게 : 89kg, 키 : 192cm, bmi 지수 : 24.14, 결과 : 정상\n",
+      "\n",
+      "사아 99 197\n",
+      "\n",
+      " 이름 : 사아, 몸무게 : 99kg, 키 : 197cm, bmi 지수 : 25.51, 결과 : 과체중\n",
+      "\n",
+      "사차 61 172\n",
+      "\n",
+      " 이름 : 사차, 몸무게 : 61kg, 키 : 172cm, bmi 지수 : 20.62, 결과 : 정상\n",
+      "\n",
+      "차차 76 162\n",
+      "\n",
+      " 이름 : 차차, 몸무게 : 76kg, 키 : 162cm, bmi 지수 : 28.96, 결과 : 과체중\n",
+      "\n",
+      "다차 83 191\n",
+      "\n",
+      " 이름 : 다차, 몸무게 : 83kg, 키 : 191cm, bmi 지수 : 22.75, 결과 : 정상\n",
+      "\n",
+      "나타 63 150\n",
+      "\n",
+      " 이름 : 나타, 몸무게 : 63kg, 키 : 150cm, bmi 지수 : 28.00, 결과 : 과체중\n",
+      "\n",
+      "하사 91 150\n",
+      "\n",
+      " 이름 : 하사, 몸무게 : 91kg, 키 : 150cm, bmi 지수 : 40.44, 결과 : 과체중\n",
+      "\n",
+      "카사 59 162\n",
+      "\n",
+      " 이름 : 카사, 몸무게 : 59kg, 키 : 162cm, bmi 지수 : 22.48, 결과 : 정상\n",
+      "\n",
+      "카차 87 197\n",
+      "\n",
+      " 이름 : 카차, 몸무게 : 87kg, 키 : 197cm, bmi 지수 : 22.42, 결과 : 정상\n",
+      "\n",
+      "카바 61 181\n",
+      "\n",
+      " 이름 : 카바, 몸무게 : 61kg, 키 : 181cm, bmi 지수 : 18.62, 결과 : 정상\n",
+      "\n",
+      "바파 58 168\n",
+      "\n",
+      " 이름 : 바파, 몸무게 : 58kg, 키 : 168cm, bmi 지수 : 20.55, 결과 : 정상\n",
+      "\n",
+      "카타 93 200\n",
+      "\n",
+      " 이름 : 카타, 몸무게 : 93kg, 키 : 200cm, bmi 지수 : 23.25, 결과 : 정상\n",
+      "\n",
+      "나나 51 194\n",
+      "\n",
+      " 이름 : 나나, 몸무게 : 51kg, 키 : 194cm, bmi 지수 : 13.55, 결과 : 저체중\n",
+      "\n",
+      "다가 92 169\n",
+      "\n",
+      " 이름 : 다가, 몸무게 : 92kg, 키 : 169cm, bmi 지수 : 32.21, 결과 : 과체중\n",
+      "\n",
+      "가나 82 145\n",
+      "\n",
+      " 이름 : 가나, 몸무게 : 82kg, 키 : 145cm, bmi 지수 : 39.00, 결과 : 과체중\n",
+      "\n",
+      "카파 77 177\n",
+      "\n",
+      " 이름 : 카파, 몸무게 : 77kg, 키 : 177cm, bmi 지수 : 24.58, 결과 : 정상\n",
+      "\n",
+      "타하 54 157\n",
+      "\n",
+      " 이름 : 타하, 몸무게 : 54kg, 키 : 157cm, bmi 지수 : 21.91, 결과 : 정상\n",
+      "\n",
+      "차바 44 165\n",
+      "\n",
+      " 이름 : 차바, 몸무게 : 44kg, 키 : 165cm, bmi 지수 : 16.16, 결과 : 저체중\n",
+      "\n",
+      "사파 58 142\n",
+      "\n",
+      " 이름 : 사파, 몸무게 : 58kg, 키 : 142cm, bmi 지수 : 28.76, 결과 : 과체중\n",
+      "\n",
+      "나카 83 157\n",
+      "\n",
+      " 이름 : 나카, 몸무게 : 83kg, 키 : 157cm, bmi 지수 : 33.67, 결과 : 과체중\n",
+      "\n",
+      "다가 59 151\n",
+      "\n",
+      " 이름 : 다가, 몸무게 : 59kg, 키 : 151cm, bmi 지수 : 25.88, 결과 : 과체중\n",
+      "\n",
+      "마카 45 144\n",
+      "\n",
+      " 이름 : 마카, 몸무게 : 45kg, 키 : 144cm, bmi 지수 : 21.70, 결과 : 정상\n",
+      "\n",
+      "마파 72 169\n",
+      "\n",
+      " 이름 : 마파, 몸무게 : 72kg, 키 : 169cm, bmi 지수 : 25.21, 결과 : 과체중\n",
+      "\n",
+      "라아 54 188\n",
+      "\n",
+      " 이름 : 라아, 몸무게 : 54kg, 키 : 188cm, bmi 지수 : 15.28, 결과 : 저체중\n",
+      "\n",
+      "타자 89 177\n",
+      "\n",
+      " 이름 : 타자, 몸무게 : 89kg, 키 : 177cm, bmi 지수 : 28.41, 결과 : 과체중\n",
+      "\n",
+      "나나 48 198\n",
+      "\n",
+      " 이름 : 나나, 몸무게 : 48kg, 키 : 198cm, bmi 지수 : 12.24, 결과 : 저체중\n",
+      "\n",
+      "파사 75 168\n",
+      "\n",
+      " 이름 : 파사, 몸무게 : 75kg, 키 : 168cm, bmi 지수 : 26.57, 결과 : 과체중\n",
+      "\n",
+      "바다 95 157\n",
+      "\n",
+      " 이름 : 바다, 몸무게 : 95kg, 키 : 157cm, bmi 지수 : 38.54, 결과 : 과체중\n",
+      "\n",
+      "아아 58 189\n",
+      "\n",
+      " 이름 : 아아, 몸무게 : 58kg, 키 : 189cm, bmi 지수 : 16.24, 결과 : 저체중\n",
+      "\n",
+      "아가 72 174\n",
+      "\n",
+      " 이름 : 아가, 몸무게 : 72kg, 키 : 174cm, bmi 지수 : 23.78, 결과 : 정상\n",
+      "\n",
+      "카아 78 156\n",
+      "\n",
+      " 이름 : 카아, 몸무게 : 78kg, 키 : 156cm, bmi 지수 : 32.05, 결과 : 과체중\n",
+      "\n",
+      "타바 50 168\n",
+      "\n",
+      " 이름 : 타바, 몸무게 : 50kg, 키 : 168cm, bmi 지수 : 17.72, 결과 : 저체중\n",
+      "\n",
+      "다사 87 163\n",
+      "\n",
+      " 이름 : 다사, 몸무게 : 87kg, 키 : 163cm, bmi 지수 : 32.74, 결과 : 과체중\n",
+      "\n",
+      "라다 83 145\n",
+      "\n",
+      " 이름 : 라다, 몸무게 : 83kg, 키 : 145cm, bmi 지수 : 39.48, 결과 : 과체중\n",
+      "\n",
+      "다라 97 140\n",
+      "\n",
+      " 이름 : 다라, 몸무게 : 97kg, 키 : 140cm, bmi 지수 : 49.49, 결과 : 과체중\n",
+      "\n",
+      "마가 88 143\n",
+      "\n",
+      " 이름 : 마가, 몸무게 : 88kg, 키 : 143cm, bmi 지수 : 43.03, 결과 : 과체중\n",
+      "\n",
+      "라파 88 183\n",
+      "\n",
+      " 이름 : 라파, 몸무게 : 88kg, 키 : 183cm, bmi 지수 : 26.28, 결과 : 과체중\n",
+      "\n",
+      "하카 51 199\n",
+      "\n",
+      " 이름 : 하카, 몸무게 : 51kg, 키 : 199cm, bmi 지수 : 12.88, 결과 : 저체중\n",
+      "\n",
+      "자다 41 172\n",
+      "\n",
+      " 이름 : 자다, 몸무게 : 41kg, 키 : 172cm, bmi 지수 : 13.86, 결과 : 저체중\n",
+      "\n",
+      "자하 70 175\n",
+      "\n",
+      " 이름 : 자하, 몸무게 : 70kg, 키 : 175cm, bmi 지수 : 22.86, 결과 : 정상\n",
+      "\n",
+      "마다 66 184\n",
+      "\n",
+      " 이름 : 마다, 몸무게 : 66kg, 키 : 184cm, bmi 지수 : 19.49, 결과 : 정상\n",
+      "\n",
+      "바자 93 173\n",
+      "\n",
+      " 이름 : 바자, 몸무게 : 93kg, 키 : 173cm, bmi 지수 : 31.07, 결과 : 과체중\n",
+      "\n",
+      "가차 100 175\n",
+      "\n",
+      " 이름 : 가차, 몸무게 : 100kg, 키 : 175cm, bmi 지수 : 32.65, 결과 : 과체중\n",
+      "\n",
+      "마아 62 151\n",
+      "\n",
+      " 이름 : 마아, 몸무게 : 62kg, 키 : 151cm, bmi 지수 : 27.19, 결과 : 과체중\n",
+      "\n",
+      "파타 45 178\n",
+      "\n",
+      " 이름 : 파타, 몸무게 : 45kg, 키 : 178cm, bmi 지수 : 14.20, 결과 : 저체중\n",
+      "\n",
+      "타라 80 165\n",
+      "\n",
+      " 이름 : 타라, 몸무게 : 80kg, 키 : 165cm, bmi 지수 : 29.38, 결과 : 과체중\n",
+      "\n",
+      "마하 66 187\n",
+      "\n",
+      " 이름 : 마하, 몸무게 : 66kg, 키 : 187cm, bmi 지수 : 18.87, 결과 : 정상\n",
+      "\n",
+      "라카 50 182\n",
+      "\n",
+      " 이름 : 라카, 몸무게 : 50kg, 키 : 182cm, bmi 지수 : 15.09, 결과 : 저체중\n",
+      "\n",
+      "카다 47 181\n",
+      "\n",
+      " 이름 : 카다, 몸무게 : 47kg, 키 : 181cm, bmi 지수 : 14.35, 결과 : 저체중\n",
+      "\n",
+      "가가 51 144\n",
+      "\n",
+      " 이름 : 가가, 몸무게 : 51kg, 키 : 144cm, bmi 지수 : 24.59, 결과 : 정상\n",
+      "\n",
+      "파아 65 189\n",
+      "\n",
+      " 이름 : 파아, 몸무게 : 65kg, 키 : 189cm, bmi 지수 : 18.20, 결과 : 저체중\n",
+      "\n",
+      "타자 67 160\n",
+      "\n",
+      " 이름 : 타자, 몸무게 : 67kg, 키 : 160cm, bmi 지수 : 26.17, 결과 : 과체중\n",
+      "\n",
+      "카아 51 197\n",
+      "\n",
+      " 이름 : 카아, 몸무게 : 51kg, 키 : 197cm, bmi 지수 : 13.14, 결과 : 저체중\n",
+      "\n",
+      "가라 79 151\n",
+      "\n",
+      " 이름 : 가라, 몸무게 : 79kg, 키 : 151cm, bmi 지수 : 34.65, 결과 : 과체중\n",
+      "\n",
+      "자아 58 192\n",
+      "\n",
+      " 이름 : 자아, 몸무게 : 58kg, 키 : 192cm, bmi 지수 : 15.73, 결과 : 저체중\n",
+      "\n",
+      "하하 44 152\n",
+      "\n",
+      " 이름 : 하하, 몸무게 : 44kg, 키 : 152cm, bmi 지수 : 19.04, 결과 : 정상\n",
+      "\n",
+      "카다 96 170\n",
+      "\n",
+      " 이름 : 카다, 몸무게 : 96kg, 키 : 170cm, bmi 지수 : 33.22, 결과 : 과체중\n",
+      "\n",
+      "마라 84 176\n",
+      "\n",
+      " 이름 : 마라, 몸무게 : 84kg, 키 : 176cm, bmi 지수 : 27.12, 결과 : 과체중\n",
+      "\n",
+      "마아 67 179\n",
+      "\n",
+      " 이름 : 마아, 몸무게 : 67kg, 키 : 179cm, bmi 지수 : 20.91, 결과 : 정상\n",
+      "\n",
+      "차다 67 147\n",
+      "\n",
+      " 이름 : 차다, 몸무게 : 67kg, 키 : 147cm, bmi 지수 : 31.01, 결과 : 과체중\n",
+      "\n",
+      "아라 71 169\n",
+      "\n",
+      " 이름 : 아라, 몸무게 : 71kg, 키 : 169cm, bmi 지수 : 24.86, 결과 : 정상\n",
+      "\n",
+      "타라 50 154\n",
+      "\n",
+      " 이름 : 타라, 몸무게 : 50kg, 키 : 154cm, bmi 지수 : 21.08, 결과 : 정상\n",
+      "\n",
+      "바다 52 186\n",
+      "\n",
+      " 이름 : 바다, 몸무게 : 52kg, 키 : 186cm, bmi 지수 : 15.03, 결과 : 저체중\n",
+      "\n",
+      "하차 40 145\n",
+      "\n",
+      " 이름 : 하차, 몸무게 : 40kg, 키 : 145cm, bmi 지수 : 19.02, 결과 : 정상\n",
+      "\n",
+      "파사 75 161\n",
+      "\n",
+      " 이름 : 파사, 몸무게 : 75kg, 키 : 161cm, bmi 지수 : 28.93, 결과 : 과체중\n",
+      "\n",
+      "가다 66 147\n",
+      "\n",
+      " 이름 : 가다, 몸무게 : 66kg, 키 : 147cm, bmi 지수 : 30.54, 결과 : 과체중\n",
+      "\n",
+      "라하 84 177\n",
+      "\n",
+      " 이름 : 라하, 몸무게 : 84kg, 키 : 177cm, bmi 지수 : 26.81, 결과 : 과체중\n",
+      "\n",
+      "라바 53 163\n",
+      "\n",
+      " 이름 : 라바, 몸무게 : 53kg, 키 : 163cm, bmi 지수 : 19.95, 결과 : 정상\n",
+      "\n",
+      "다사 61 155\n",
+      "\n",
+      " 이름 : 다사, 몸무게 : 61kg, 키 : 155cm, bmi 지수 : 25.39, 결과 : 과체중\n",
+      "\n",
+      "아바 57 192\n",
+      "\n",
+      " 이름 : 아바, 몸무게 : 57kg, 키 : 192cm, bmi 지수 : 15.46, 결과 : 저체중\n",
+      "\n",
+      "마다 55 172\n",
+      "\n",
+      " 이름 : 마다, 몸무게 : 55kg, 키 : 172cm, bmi 지수 : 18.59, 결과 : 정상\n",
+      "\n",
+      "타카 81 159\n",
+      "\n",
+      " 이름 : 타카, 몸무게 : 81kg, 키 : 159cm, bmi 지수 : 32.04, 결과 : 과체중\n",
+      "\n",
+      "타라 74 191\n",
+      "\n",
+      " 이름 : 타라, 몸무게 : 74kg, 키 : 191cm, bmi 지수 : 20.28, 결과 : 정상\n",
+      "\n",
+      "다차 62 142\n",
+      "\n",
+      " 이름 : 다차, 몸무게 : 62kg, 키 : 142cm, bmi 지수 : 30.75, 결과 : 과체중\n",
+      "\n",
+      "가하 48 171\n",
+      "\n",
+      " 이름 : 가하, 몸무게 : 48kg, 키 : 171cm, bmi 지수 : 16.42, 결과 : 저체중\n",
+      "\n",
+      "차바 94 166\n",
+      "\n",
+      " 이름 : 차바, 몸무게 : 94kg, 키 : 166cm, bmi 지수 : 34.11, 결과 : 과체중\n",
+      "\n",
+      "나파 79 180\n",
+      "\n",
+      " 이름 : 나파, 몸무게 : 79kg, 키 : 180cm, bmi 지수 : 24.38, 결과 : 정상\n",
+      "\n",
+      "차사 80 140\n",
+      "\n",
+      " 이름 : 차사, 몸무게 : 80kg, 키 : 140cm, bmi 지수 : 40.82, 결과 : 과체중\n",
+      "\n",
+      "하다 51 196\n",
+      "\n",
+      " 이름 : 하다, 몸무게 : 51kg, 키 : 196cm, bmi 지수 : 13.28, 결과 : 저체중\n",
+      "\n",
+      "바나 54 148\n",
+      "\n",
+      " 이름 : 바나, 몸무게 : 54kg, 키 : 148cm, bmi 지수 : 24.65, 결과 : 정상\n",
+      "\n",
+      "마카 66 163\n",
+      "\n",
+      " 이름 : 마카, 몸무게 : 66kg, 키 : 163cm, bmi 지수 : 24.84, 결과 : 정상\n",
+      "\n",
+      "하나 78 169\n",
+      "\n",
+      " 이름 : 하나, 몸무게 : 78kg, 키 : 169cm, bmi 지수 : 27.31, 결과 : 과체중\n",
+      "\n",
+      "마나 85 171\n",
+      "\n",
+      " 이름 : 마나, 몸무게 : 85kg, 키 : 171cm, bmi 지수 : 29.07, 결과 : 과체중\n",
+      "\n",
+      "자마 74 152\n",
+      "\n",
+      " 이름 : 자마, 몸무게 : 74kg, 키 : 152cm, bmi 지수 : 32.03, 결과 : 과체중\n",
+      "\n",
+      "파나 41 170\n",
+      "\n",
+      " 이름 : 파나, 몸무게 : 41kg, 키 : 170cm, bmi 지수 : 14.19, 결과 : 저체중\n",
+      "\n",
+      "라나 88 149\n",
+      "\n",
+      " 이름 : 라나, 몸무게 : 88kg, 키 : 149cm, bmi 지수 : 39.64, 결과 : 과체중\n",
+      "\n",
+      "다가 73 158\n",
+      "\n",
+      " 이름 : 다가, 몸무게 : 73kg, 키 : 158cm, bmi 지수 : 29.24, 결과 : 과체중\n",
+      "\n",
+      "자아 78 163\n",
+      "\n",
+      " 이름 : 자아, 몸무게 : 78kg, 키 : 163cm, bmi 지수 : 29.36, 결과 : 과체중\n",
+      "\n",
+      "아나 47 167\n",
+      "\n",
+      " 이름 : 아나, 몸무게 : 47kg, 키 : 167cm, bmi 지수 : 16.85, 결과 : 저체중\n",
+      "\n",
+      "바마 54 144\n",
+      "\n",
+      " 이름 : 바마, 몸무게 : 54kg, 키 : 144cm, bmi 지수 : 26.04, 결과 : 과체중\n",
+      "\n",
+      "사바 47 177\n",
+      "\n",
+      " 이름 : 사바, 몸무게 : 47kg, 키 : 177cm, bmi 지수 : 15.00, 결과 : 저체중\n",
+      "\n",
+      "자타 58 156\n",
+      "\n",
+      " 이름 : 자타, 몸무게 : 58kg, 키 : 156cm, bmi 지수 : 23.83, 결과 : 정상\n",
+      "\n",
+      "차나 52 177\n",
+      "\n",
+      " 이름 : 차나, 몸무게 : 52kg, 키 : 177cm, bmi 지수 : 16.60, 결과 : 저체중\n",
+      "\n",
+      "타카 69 174\n",
+      "\n",
+      " 이름 : 타카, 몸무게 : 69kg, 키 : 174cm, bmi 지수 : 22.79, 결과 : 정상\n",
+      "\n",
+      "사자 54 178\n",
+      "\n",
+      " 이름 : 사자, 몸무게 : 54kg, 키 : 178cm, bmi 지수 : 17.04, 결과 : 저체중\n",
+      "\n",
+      "바나 49 143\n",
+      "\n",
+      " 이름 : 바나, 몸무게 : 49kg, 키 : 143cm, bmi 지수 : 23.96, 결과 : 정상\n",
+      "\n",
+      "라하 87 177\n",
+      "\n",
+      " 이름 : 라하, 몸무게 : 87kg, 키 : 177cm, bmi 지수 : 27.77, 결과 : 과체중\n",
+      "\n",
+      "마차 73 199\n",
+      "\n",
+      " 이름 : 마차, 몸무게 : 73kg, 키 : 199cm, bmi 지수 : 18.43, 결과 : 저체중\n",
+      "\n",
+      "아자 61 152\n",
+      "\n",
+      " 이름 : 아자, 몸무게 : 61kg, 키 : 152cm, bmi 지수 : 26.40, 결과 : 과체중\n",
+      "\n",
+      "마파 41 177\n",
+      "\n",
+      " 이름 : 마파, 몸무게 : 41kg, 키 : 177cm, bmi 지수 : 13.09, 결과 : 저체중\n",
+      "\n",
+      "카나 76 145\n",
+      "\n",
+      " 이름 : 카나, 몸무게 : 76kg, 키 : 145cm, bmi 지수 : 36.15, 결과 : 과체중\n",
+      "\n",
+      "마타 57 194\n",
+      "\n",
+      " 이름 : 마타, 몸무게 : 57kg, 키 : 194cm, bmi 지수 : 15.15, 결과 : 저체중\n",
+      "\n",
+      "차파 90 169\n",
+      "\n",
+      " 이름 : 차파, 몸무게 : 90kg, 키 : 169cm, bmi 지수 : 31.51, 결과 : 과체중\n",
+      "\n",
+      "가아 58 194\n",
+      "\n",
+      " 이름 : 가아, 몸무게 : 58kg, 키 : 194cm, bmi 지수 : 15.41, 결과 : 저체중\n",
+      "\n",
+      "나다 85 171\n",
+      "\n",
+      " 이름 : 나다, 몸무게 : 85kg, 키 : 171cm, bmi 지수 : 29.07, 결과 : 과체중\n",
+      "\n",
+      "다라 89 171\n",
+      "\n",
+      " 이름 : 다라, 몸무게 : 89kg, 키 : 171cm, bmi 지수 : 30.44, 결과 : 과체중\n",
+      "\n",
+      "다하 87 152\n",
+      "\n",
+      " 이름 : 다하, 몸무게 : 87kg, 키 : 152cm, bmi 지수 : 37.66, 결과 : 과체중\n",
+      "\n",
+      "차나 71 147\n",
+      "\n",
+      " 이름 : 차나, 몸무게 : 71kg, 키 : 147cm, bmi 지수 : 32.86, 결과 : 과체중\n",
+      "\n",
+      "바마 71 143\n",
+      "\n",
+      " 이름 : 바마, 몸무게 : 71kg, 키 : 143cm, bmi 지수 : 34.72, 결과 : 과체중\n",
+      "\n",
+      "자가 64 176\n",
+      "\n",
+      " 이름 : 자가, 몸무게 : 64kg, 키 : 176cm, bmi 지수 : 20.66, 결과 : 정상\n",
+      "\n",
+      "파나 75 183\n",
+      "\n",
+      " 이름 : 파나, 몸무게 : 75kg, 키 : 183cm, bmi 지수 : 22.40, 결과 : 정상\n",
+      "\n",
+      "라파 99 185\n",
+      "\n",
+      " 이름 : 라파, 몸무게 : 99kg, 키 : 185cm, bmi 지수 : 28.93, 결과 : 과체중\n",
+      "\n",
+      "라사 91 173\n",
+      "\n",
+      " 이름 : 라사, 몸무게 : 91kg, 키 : 173cm, bmi 지수 : 30.41, 결과 : 과체중\n",
+      "\n",
+      "바다 59 191\n",
+      "\n",
+      " 이름 : 바다, 몸무게 : 59kg, 키 : 191cm, bmi 지수 : 16.17, 결과 : 저체중\n",
+      "\n",
+      "자하 56 179\n",
+      "\n",
+      " 이름 : 자하, 몸무게 : 56kg, 키 : 179cm, bmi 지수 : 17.48, 결과 : 저체중\n",
+      "\n",
+      "아나 79 163\n",
+      "\n",
+      " 이름 : 아나, 몸무게 : 79kg, 키 : 163cm, bmi 지수 : 29.73, 결과 : 과체중\n",
+      "\n",
+      "마아 67 183\n",
+      "\n",
+      " 이름 : 마아, 몸무게 : 67kg, 키 : 183cm, bmi 지수 : 20.01, 결과 : 정상\n",
+      "\n",
+      "자파 99 198\n",
+      "\n",
+      " 이름 : 자파, 몸무게 : 99kg, 키 : 198cm, bmi 지수 : 25.25, 결과 : 과체중\n",
+      "\n",
+      "마사 89 154\n",
+      "\n",
+      " 이름 : 마사, 몸무게 : 89kg, 키 : 154cm, bmi 지수 : 37.53, 결과 : 과체중\n",
+      "\n",
+      "사다 90 190\n",
+      "\n",
+      " 이름 : 사다, 몸무게 : 90kg, 키 : 190cm, bmi 지수 : 24.93, 결과 : 정상\n",
+      "\n",
+      "타마 76 152\n",
+      "\n",
+      " 이름 : 타마, 몸무게 : 76kg, 키 : 152cm, bmi 지수 : 32.89, 결과 : 과체중\n",
+      "\n",
+      "바가 82 176\n",
+      "\n",
+      " 이름 : 바가, 몸무게 : 82kg, 키 : 176cm, bmi 지수 : 26.47, 결과 : 과체중\n",
+      "\n",
+      "마타 47 180\n",
+      "\n",
+      " 이름 : 마타, 몸무게 : 47kg, 키 : 180cm, bmi 지수 : 14.51, 결과 : 저체중\n",
+      "\n",
+      "마하 93 200\n",
+      "\n",
+      " 이름 : 마하, 몸무게 : 93kg, 키 : 200cm, bmi 지수 : 23.25, 결과 : 정상\n",
+      "\n",
+      "아파 100 183\n",
+      "\n",
+      " 이름 : 아파, 몸무게 : 100kg, 키 : 183cm, bmi 지수 : 29.86, 결과 : 과체중\n",
+      "\n",
+      "카가 81 178\n",
+      "\n",
+      " 이름 : 카가, 몸무게 : 81kg, 키 : 178cm, bmi 지수 : 25.56, 결과 : 과체중\n",
+      "\n",
+      "바하 96 145\n",
+      "\n",
+      " 이름 : 바하, 몸무게 : 96kg, 키 : 145cm, bmi 지수 : 45.66, 결과 : 과체중\n",
+      "\n",
+      "하마 61 185\n",
+      "\n",
+      " 이름 : 하마, 몸무게 : 61kg, 키 : 185cm, bmi 지수 : 17.82, 결과 : 저체중\n",
+      "\n",
+      "바마 58 151\n",
+      "\n",
+      " 이름 : 바마, 몸무게 : 58kg, 키 : 151cm, bmi 지수 : 25.44, 결과 : 과체중\n",
+      "\n",
+      "카사 100 179\n",
+      "\n",
+      " 이름 : 카사, 몸무게 : 100kg, 키 : 179cm, bmi 지수 : 31.21, 결과 : 과체중\n",
+      "\n",
+      "마카 67 159\n",
+      "\n",
+      " 이름 : 마카, 몸무게 : 67kg, 키 : 159cm, bmi 지수 : 26.50, 결과 : 과체중\n",
+      "\n",
+      "마나 74 195\n",
+      "\n",
+      " 이름 : 마나, 몸무게 : 74kg, 키 : 195cm, bmi 지수 : 19.46, 결과 : 정상\n",
+      "\n",
+      "하다 76 162\n",
+      "\n",
+      " 이름 : 하다, 몸무게 : 76kg, 키 : 162cm, bmi 지수 : 28.96, 결과 : 과체중\n",
+      "\n",
+      "타자 69 177\n",
+      "\n",
+      " 이름 : 타자, 몸무게 : 69kg, 키 : 177cm, bmi 지수 : 22.02, 결과 : 정상\n",
+      "\n",
+      "바라 59 149\n",
+      "\n",
+      " 이름 : 바라, 몸무게 : 59kg, 키 : 149cm, bmi 지수 : 26.58, 결과 : 과체중\n",
+      "\n",
+      "자파 58 181\n",
+      "\n",
+      " 이름 : 자파, 몸무게 : 58kg, 키 : 181cm, bmi 지수 : 17.70, 결과 : 저체중\n",
+      "\n",
+      "바나 79 154\n",
+      "\n",
+      " 이름 : 바나, 몸무게 : 79kg, 키 : 154cm, bmi 지수 : 33.31, 결과 : 과체중\n",
+      "\n",
+      "차아 91 163\n",
+      "\n",
+      " 이름 : 차아, 몸무게 : 91kg, 키 : 163cm, bmi 지수 : 34.25, 결과 : 과체중\n",
+      "\n",
+      "카라 63 179\n",
+      "\n",
+      " 이름 : 카라, 몸무게 : 63kg, 키 : 179cm, bmi 지수 : 19.66, 결과 : 정상\n",
+      "\n",
+      "가카 60 197\n",
+      "\n",
+      " 이름 : 가카, 몸무게 : 60kg, 키 : 197cm, bmi 지수 : 15.46, 결과 : 저체중\n",
+      "\n",
+      "가바 54 185\n",
+      "\n",
+      " 이름 : 가바, 몸무게 : 54kg, 키 : 185cm, bmi 지수 : 15.78, 결과 : 저체중\n",
+      "\n",
+      "바나 58 157\n",
+      "\n",
+      " 이름 : 바나, 몸무게 : 58kg, 키 : 157cm, bmi 지수 : 23.53, 결과 : 정상\n",
+      "\n",
+      "차바 57 183\n",
+      "\n",
+      " 이름 : 차바, 몸무게 : 57kg, 키 : 183cm, bmi 지수 : 17.02, 결과 : 저체중\n",
+      "\n",
+      "가사 66 163\n",
+      "\n",
+      " 이름 : 가사, 몸무게 : 66kg, 키 : 163cm, bmi 지수 : 24.84, 결과 : 정상\n",
+      "\n",
+      "바차 74 142\n",
+      "\n",
+      " 이름 : 바차, 몸무게 : 74kg, 키 : 142cm, bmi 지수 : 36.70, 결과 : 과체중\n",
+      "\n",
+      "다파 56 183\n",
+      "\n",
+      " 이름 : 다파, 몸무게 : 56kg, 키 : 183cm, bmi 지수 : 16.72, 결과 : 저체중\n",
+      "\n",
+      "아가 70 141\n",
+      "\n",
+      " 이름 : 아가, 몸무게 : 70kg, 키 : 141cm, bmi 지수 : 35.21, 결과 : 과체중\n",
+      "\n",
+      "가차 46 141\n",
+      "\n",
+      " 이름 : 가차, 몸무게 : 46kg, 키 : 141cm, bmi 지수 : 23.14, 결과 : 정상\n",
+      "\n",
+      "라아 76 140\n",
+      "\n",
+      " 이름 : 라아, 몸무게 : 76kg, 키 : 140cm, bmi 지수 : 38.78, 결과 : 과체중\n",
+      "\n",
+      "하타 70 158\n",
+      "\n",
+      " 이름 : 하타, 몸무게 : 70kg, 키 : 158cm, bmi 지수 : 28.04, 결과 : 과체중\n",
+      "\n",
+      "다사 90 177\n",
+      "\n",
+      " 이름 : 다사, 몸무게 : 90kg, 키 : 177cm, bmi 지수 : 28.73, 결과 : 과체중\n",
+      "\n",
+      "아자 54 152\n",
+      "\n",
+      " 이름 : 아자, 몸무게 : 54kg, 키 : 152cm, bmi 지수 : 23.37, 결과 : 정상\n",
+      "\n",
+      "라라 40 198\n",
+      "\n",
+      " 이름 : 라라, 몸무게 : 40kg, 키 : 198cm, bmi 지수 : 10.20, 결과 : 저체중\n",
+      "\n",
+      "하다 77 164\n",
+      "\n",
+      " 이름 : 하다, 몸무게 : 77kg, 키 : 164cm, bmi 지수 : 28.63, 결과 : 과체중\n",
+      "\n",
+      "가카 89 183\n",
+      "\n",
+      " 이름 : 가카, 몸무게 : 89kg, 키 : 183cm, bmi 지수 : 26.58, 결과 : 과체중\n",
+      "\n",
+      "자하 94 145\n",
+      "\n",
+      " 이름 : 자하, 몸무게 : 94kg, 키 : 145cm, bmi 지수 : 44.71, 결과 : 과체중\n",
+      "\n",
+      "가타 85 177\n",
+      "\n",
+      " 이름 : 가타, 몸무게 : 85kg, 키 : 177cm, bmi 지수 : 27.13, 결과 : 과체중\n",
+      "\n",
+      "나아 92 162\n",
+      "\n",
+      " 이름 : 나아, 몸무게 : 92kg, 키 : 162cm, bmi 지수 : 35.06, 결과 : 과체중\n",
+      "\n",
+      "다카 77 171\n",
+      "\n",
+      " 이름 : 다카, 몸무게 : 77kg, 키 : 171cm, bmi 지수 : 26.33, 결과 : 과체중\n",
+      "\n",
+      "다마 88 156\n",
+      "\n",
+      " 이름 : 다마, 몸무게 : 88kg, 키 : 156cm, bmi 지수 : 36.16, 결과 : 과체중\n",
+      "\n",
+      "바라 100 193\n",
+      "\n",
+      " 이름 : 바라, 몸무게 : 100kg, 키 : 193cm, bmi 지수 : 26.85, 결과 : 과체중\n",
+      "\n",
+      "하카 55 194\n",
+      "\n",
+      " 이름 : 하카, 몸무게 : 55kg, 키 : 194cm, bmi 지수 : 14.61, 결과 : 저체중\n",
+      "\n",
+      "자파 42 150\n",
+      "\n",
+      " 이름 : 자파, 몸무게 : 42kg, 키 : 150cm, bmi 지수 : 18.67, 결과 : 정상\n",
+      "\n",
+      "아자 82 155\n",
+      "\n",
+      " 이름 : 아자, 몸무게 : 82kg, 키 : 155cm, bmi 지수 : 34.13, 결과 : 과체중\n",
+      "\n",
+      "마마 46 144\n",
+      "\n",
+      " 이름 : 마마, 몸무게 : 46kg, 키 : 144cm, bmi 지수 : 22.18, 결과 : 정상\n",
+      "\n",
+      "카가 96 147\n",
+      "\n",
+      " 이름 : 카가, 몸무게 : 96kg, 키 : 147cm, bmi 지수 : 44.43, 결과 : 과체중\n",
+      "\n",
+      "마자 50 148\n",
+      "\n",
+      " 이름 : 마자, 몸무게 : 50kg, 키 : 148cm, bmi 지수 : 22.83, 결과 : 정상\n",
+      "\n",
+      "아라 47 160\n",
+      "\n",
+      " 이름 : 아라, 몸무게 : 47kg, 키 : 160cm, bmi 지수 : 18.36, 결과 : 저체중\n",
+      "\n",
+      "카라 99 176\n",
+      "\n",
+      " 이름 : 카라, 몸무게 : 99kg, 키 : 176cm, bmi 지수 : 31.96, 결과 : 과체중\n",
+      "\n",
+      "타바 56 186\n",
+      "\n",
+      " 이름 : 타바, 몸무게 : 56kg, 키 : 186cm, bmi 지수 : 16.19, 결과 : 저체중\n",
+      "\n",
+      "타아 64 173\n",
+      "\n",
+      " 이름 : 타아, 몸무게 : 64kg, 키 : 173cm, bmi 지수 : 21.38, 결과 : 정상\n",
+      "\n",
+      "바파 86 192\n",
+      "\n",
+      " 이름 : 바파, 몸무게 : 86kg, 키 : 192cm, bmi 지수 : 23.33, 결과 : 정상\n",
+      "\n",
+      "마사 78 176\n",
+      "\n",
+      " 이름 : 마사, 몸무게 : 78kg, 키 : 176cm, bmi 지수 : 25.18, 결과 : 과체중\n",
+      "\n",
+      "아파 62 193\n",
+      "\n",
+      " 이름 : 아파, 몸무게 : 62kg, 키 : 193cm, bmi 지수 : 16.64, 결과 : 저체중\n",
+      "\n",
+      "카바 91 164\n",
+      "\n",
+      " 이름 : 카바, 몸무게 : 91kg, 키 : 164cm, bmi 지수 : 33.83, 결과 : 과체중\n",
+      "\n",
+      "마타 62 161\n",
+      "\n",
+      " 이름 : 마타, 몸무게 : 62kg, 키 : 161cm, bmi 지수 : 23.92, 결과 : 정상\n",
+      "\n",
+      "마가 92 199\n",
+      "\n",
+      " 이름 : 마가, 몸무게 : 92kg, 키 : 199cm, bmi 지수 : 23.23, 결과 : 정상\n",
+      "\n",
+      "라바 50 177\n",
+      "\n",
+      " 이름 : 라바, 몸무게 : 50kg, 키 : 177cm, bmi 지수 : 15.96, 결과 : 저체중\n",
+      "\n",
+      "라라 68 158\n",
+      "\n",
+      " 이름 : 라라, 몸무게 : 68kg, 키 : 158cm, bmi 지수 : 27.24, 결과 : 과체중\n",
+      "\n",
+      "차파 40 176\n",
+      "\n",
+      " 이름 : 차파, 몸무게 : 40kg, 키 : 176cm, bmi 지수 : 12.91, 결과 : 저체중\n",
+      "\n",
+      "타나 58 147\n",
+      "\n",
+      " 이름 : 타나, 몸무게 : 58kg, 키 : 147cm, bmi 지수 : 26.84, 결과 : 과체중\n",
+      "\n",
+      "라하 83 158\n",
+      "\n",
+      " 이름 : 라하, 몸무게 : 83kg, 키 : 158cm, bmi 지수 : 33.25, 결과 : 과체중\n",
+      "\n",
+      "자바 79 167\n",
+      "\n",
+      " 이름 : 자바, 몸무게 : 79kg, 키 : 167cm, bmi 지수 : 28.33, 결과 : 과체중\n",
+      "\n",
+      "하아 53 140\n",
+      "\n",
+      " 이름 : 하아, 몸무게 : 53kg, 키 : 140cm, bmi 지수 : 27.04, 결과 : 과체중\n",
+      "\n",
+      "다라 93 185\n",
+      "\n",
+      " 이름 : 다라, 몸무게 : 93kg, 키 : 185cm, bmi 지수 : 27.17, 결과 : 과체중\n",
+      "\n",
+      "다나 78 184\n",
+      "\n",
+      " 이름 : 다나, 몸무게 : 78kg, 키 : 184cm, bmi 지수 : 23.04, 결과 : 정상\n",
+      "\n",
+      "아다 53 176\n",
+      "\n",
+      " 이름 : 아다, 몸무게 : 53kg, 키 : 176cm, bmi 지수 : 17.11, 결과 : 저체중\n",
+      "\n",
+      "타다 55 146\n",
+      "\n",
+      " 이름 : 타다, 몸무게 : 55kg, 키 : 146cm, bmi 지수 : 25.80, 결과 : 과체중\n",
+      "\n",
+      "하자 67 198\n",
+      "\n",
+      " 이름 : 하자, 몸무게 : 67kg, 키 : 198cm, bmi 지수 : 17.09, 결과 : 저체중\n",
+      "\n",
+      "사바 47 149\n",
+      "\n",
+      " 이름 : 사바, 몸무게 : 47kg, 키 : 149cm, bmi 지수 : 21.17, 결과 : 정상\n",
+      "\n",
+      "타나 83 185\n",
+      "\n",
+      " 이름 : 타나, 몸무게 : 83kg, 키 : 185cm, bmi 지수 : 24.25, 결과 : 정상\n",
+      "\n",
+      "타자 43 194\n",
+      "\n",
+      " 이름 : 타자, 몸무게 : 43kg, 키 : 194cm, bmi 지수 : 11.43, 결과 : 저체중\n",
+      "\n",
+      "파파 52 200\n",
+      "\n",
+      " 이름 : 파파, 몸무게 : 52kg, 키 : 200cm, bmi 지수 : 13.00, 결과 : 저체중\n",
+      "\n",
+      "차타 84 144\n",
+      "\n",
+      " 이름 : 차타, 몸무게 : 84kg, 키 : 144cm, bmi 지수 : 40.51, 결과 : 과체중\n",
+      "\n",
+      "가가 92 196\n",
+      "\n",
+      " 이름 : 가가, 몸무게 : 92kg, 키 : 196cm, bmi 지수 : 23.95, 결과 : 정상\n",
+      "\n",
+      "라하 47 144\n",
+      "\n",
+      " 이름 : 라하, 몸무게 : 47kg, 키 : 144cm, bmi 지수 : 22.67, 결과 : 정상\n",
+      "\n",
+      "차사 60 174\n",
+      "\n",
+      " 이름 : 차사, 몸무게 : 60kg, 키 : 174cm, bmi 지수 : 19.82, 결과 : 정상\n",
+      "\n",
+      "마타 65 179\n",
+      "\n",
+      " 이름 : 마타, 몸무게 : 65kg, 키 : 179cm, bmi 지수 : 20.29, 결과 : 정상\n",
+      "\n",
+      "다사 63 148\n",
+      "\n",
+      " 이름 : 다사, 몸무게 : 63kg, 키 : 148cm, bmi 지수 : 28.76, 결과 : 과체중\n",
+      "\n",
+      "카아 79 142\n",
+      "\n",
+      " 이름 : 카아, 몸무게 : 79kg, 키 : 142cm, bmi 지수 : 39.18, 결과 : 과체중\n",
+      "\n",
+      "라카 73 161\n",
+      "\n",
+      " 이름 : 라카, 몸무게 : 73kg, 키 : 161cm, bmi 지수 : 28.16, 결과 : 과체중\n",
+      "\n",
+      "카아 92 141\n",
+      "\n",
+      " 이름 : 카아, 몸무게 : 92kg, 키 : 141cm, bmi 지수 : 46.28, 결과 : 과체중\n",
+      "\n",
+      "라라 92 151\n",
+      "\n",
+      " 이름 : 라라, 몸무게 : 92kg, 키 : 151cm, bmi 지수 : 40.35, 결과 : 과체중\n",
+      "\n",
+      "차카 71 183\n",
+      "\n",
+      " 이름 : 차카, 몸무게 : 71kg, 키 : 183cm, bmi 지수 : 21.20, 결과 : 정상\n",
+      "\n",
+      "하타 91 146\n",
+      "\n",
+      " 이름 : 하타, 몸무게 : 91kg, 키 : 146cm, bmi 지수 : 42.69, 결과 : 과체중\n",
+      "\n",
+      "사사 82 194\n",
+      "\n",
+      " 이름 : 사사, 몸무게 : 82kg, 키 : 194cm, bmi 지수 : 21.79, 결과 : 정상\n",
+      "\n",
+      "바파 58 180\n",
+      "\n",
+      " 이름 : 바파, 몸무게 : 58kg, 키 : 180cm, bmi 지수 : 17.90, 결과 : 저체중\n",
+      "\n",
+      "카가 40 176\n",
+      "\n",
+      " 이름 : 카가, 몸무게 : 40kg, 키 : 176cm, bmi 지수 : 12.91, 결과 : 저체중\n",
+      "\n",
+      "차다 72 182\n",
+      "\n",
+      " 이름 : 차다, 몸무게 : 72kg, 키 : 182cm, bmi 지수 : 21.74, 결과 : 정상\n",
+      "\n",
+      "파가 95 163\n",
+      "\n",
+      " 이름 : 파가, 몸무게 : 95kg, 키 : 163cm, bmi 지수 : 35.76, 결과 : 과체중\n",
+      "\n",
+      "파아 82 196\n",
+      "\n",
+      " 이름 : 파아, 몸무게 : 82kg, 키 : 196cm, bmi 지수 : 21.35, 결과 : 정상\n",
+      "\n",
+      "타가 40 143\n",
+      "\n",
+      " 이름 : 타가, 몸무게 : 40kg, 키 : 143cm, bmi 지수 : 19.56, 결과 : 정상\n",
+      "\n",
+      "나나 69 176\n",
+      "\n",
+      " 이름 : 나나, 몸무게 : 69kg, 키 : 176cm, bmi 지수 : 22.28, 결과 : 정상\n",
+      "\n",
+      "카카 98 171\n",
+      "\n",
+      " 이름 : 카카, 몸무게 : 98kg, 키 : 171cm, bmi 지수 : 33.51, 결과 : 과체중\n",
+      "\n",
+      "차라 77 198\n",
+      "\n",
+      " 이름 : 차라, 몸무게 : 77kg, 키 : 198cm, bmi 지수 : 19.64, 결과 : 정상\n",
+      "\n",
+      "파바 54 140\n",
+      "\n",
+      " 이름 : 파바, 몸무게 : 54kg, 키 : 140cm, bmi 지수 : 27.55, 결과 : 과체중\n",
+      "\n",
+      "다다 96 198\n",
+      "\n",
+      " 이름 : 다다, 몸무게 : 96kg, 키 : 198cm, bmi 지수 : 24.49, 결과 : 정상\n",
+      "\n",
+      "바파 77 141\n",
+      "\n",
+      " 이름 : 바파, 몸무게 : 77kg, 키 : 141cm, bmi 지수 : 38.73, 결과 : 과체중\n",
+      "\n",
+      "자라 95 171\n",
+      "\n",
+      " 이름 : 자라, 몸무게 : 95kg, 키 : 171cm, bmi 지수 : 32.49, 결과 : 과체중\n",
+      "\n",
+      "파마 90 178\n",
+      "\n",
+      " 이름 : 파마, 몸무게 : 90kg, 키 : 178cm, bmi 지수 : 28.41, 결과 : 과체중\n",
+      "\n",
+      "다바 100 168\n",
+      "\n",
+      " 이름 : 다바, 몸무게 : 100kg, 키 : 168cm, bmi 지수 : 35.43, 결과 : 과체중\n",
+      "\n",
+      "타타 94 187\n",
+      "\n",
+      " 이름 : 타타, 몸무게 : 94kg, 키 : 187cm, bmi 지수 : 26.88, 결과 : 과체중\n",
+      "\n",
+      "타파 90 147\n",
+      "\n",
+      " 이름 : 타파, 몸무게 : 90kg, 키 : 147cm, bmi 지수 : 41.65, 결과 : 과체중\n",
+      "\n",
+      "차사 43 142\n",
+      "\n",
+      " 이름 : 차사, 몸무게 : 43kg, 키 : 142cm, bmi 지수 : 21.33, 결과 : 정상\n",
+      "\n",
+      "자마 64 194\n",
+      "\n",
+      " 이름 : 자마, 몸무게 : 64kg, 키 : 194cm, bmi 지수 : 17.00, 결과 : 저체중\n",
+      "\n",
+      "가파 89 192\n",
+      "\n",
+      " 이름 : 가파, 몸무게 : 89kg, 키 : 192cm, bmi 지수 : 24.14, 결과 : 정상\n",
+      "\n",
+      "파파 93 189\n",
+      "\n",
+      " 이름 : 파파, 몸무게 : 93kg, 키 : 189cm, bmi 지수 : 26.04, 결과 : 과체중\n",
+      "\n",
+      "라가 48 182\n",
+      "\n",
+      " 이름 : 라가, 몸무게 : 48kg, 키 : 182cm, bmi 지수 : 14.49, 결과 : 저체중\n",
+      "\n",
+      "마타 49 166\n",
+      "\n",
+      " 이름 : 마타, 몸무게 : 49kg, 키 : 166cm, bmi 지수 : 17.78, 결과 : 저체중\n",
+      "\n",
+      "다타 84 157\n",
+      "\n",
+      " 이름 : 다타, 몸무게 : 84kg, 키 : 157cm, bmi 지수 : 34.08, 결과 : 과체중\n",
+      "\n",
+      "타가 76 160\n",
+      "\n",
+      " 이름 : 타가, 몸무게 : 76kg, 키 : 160cm, bmi 지수 : 29.69, 결과 : 과체중\n",
+      "\n",
+      "하자 46 140\n",
+      "\n",
+      " 이름 : 하자, 몸무게 : 46kg, 키 : 140cm, bmi 지수 : 23.47, 결과 : 정상\n",
+      "\n",
+      "자다 45 182\n",
+      "\n",
+      " 이름 : 자다, 몸무게 : 45kg, 키 : 182cm, bmi 지수 : 13.59, 결과 : 저체중\n",
+      "\n",
+      "카타 49 162\n",
+      "\n",
+      " 이름 : 카타, 몸무게 : 49kg, 키 : 162cm, bmi 지수 : 18.67, 결과 : 정상\n",
+      "\n",
+      "카아 85 170\n",
+      "\n",
+      " 이름 : 카아, 몸무게 : 85kg, 키 : 170cm, bmi 지수 : 29.41, 결과 : 과체중\n",
+      "\n",
+      "바자 56 157\n",
+      "\n",
+      " 이름 : 바자, 몸무게 : 56kg, 키 : 157cm, bmi 지수 : 22.72, 결과 : 정상\n",
+      "\n",
+      "아라 73 180\n",
+      "\n",
+      " 이름 : 아라, 몸무게 : 73kg, 키 : 180cm, bmi 지수 : 22.53, 결과 : 정상\n",
+      "\n",
+      "라아 98 200\n",
+      "\n",
+      " 이름 : 라아, 몸무게 : 98kg, 키 : 200cm, bmi 지수 : 24.50, 결과 : 정상\n",
+      "\n",
+      "아차 55 143\n",
+      "\n",
+      " 이름 : 아차, 몸무게 : 55kg, 키 : 143cm, bmi 지수 : 26.90, 결과 : 과체중\n",
+      "\n",
+      "파라 58 149\n",
+      "\n",
+      " 이름 : 파라, 몸무게 : 58kg, 키 : 149cm, bmi 지수 : 26.12, 결과 : 과체중\n",
+      "\n",
+      "파바 44 186\n",
+      "\n",
+      " 이름 : 파바, 몸무게 : 44kg, 키 : 186cm, bmi 지수 : 12.72, 결과 : 저체중\n",
+      "\n",
+      "하자 83 197\n",
+      "\n",
+      " 이름 : 하자, 몸무게 : 83kg, 키 : 197cm, bmi 지수 : 21.39, 결과 : 정상\n",
+      "\n",
+      "아차 67 155\n",
+      "\n",
+      " 이름 : 아차, 몸무게 : 67kg, 키 : 155cm, bmi 지수 : 27.89, 결과 : 과체중\n",
+      "\n",
+      "카하 51 169\n",
+      "\n",
+      " 이름 : 카하, 몸무게 : 51kg, 키 : 169cm, bmi 지수 : 17.86, 결과 : 저체중\n",
+      "\n",
+      "하바 56 144\n",
+      "\n",
+      " 이름 : 하바, 몸무게 : 56kg, 키 : 144cm, bmi 지수 : 27.01, 결과 : 과체중\n",
+      "\n",
+      "타카 91 155\n",
+      "\n",
+      " 이름 : 타카, 몸무게 : 91kg, 키 : 155cm, bmi 지수 : 37.88, 결과 : 과체중\n",
+      "\n",
+      "차마 86 158\n",
+      "\n",
+      " 이름 : 차마, 몸무게 : 86kg, 키 : 158cm, bmi 지수 : 34.45, 결과 : 과체중\n",
+      "\n",
+      "차아 96 154\n",
+      "\n",
+      " 이름 : 차아, 몸무게 : 96kg, 키 : 154cm, bmi 지수 : 40.48, 결과 : 과체중\n",
+      "\n",
+      "다사 43 144\n",
+      "\n",
+      " 이름 : 다사, 몸무게 : 43kg, 키 : 144cm, bmi 지수 : 20.74, 결과 : 정상\n",
+      "\n",
+      "파다 70 200\n",
+      "\n",
+      " 이름 : 파다, 몸무게 : 70kg, 키 : 200cm, bmi 지수 : 17.50, 결과 : 저체중\n",
+      "\n",
+      "나아 60 153\n",
+      "\n",
+      " 이름 : 나아, 몸무게 : 60kg, 키 : 153cm, bmi 지수 : 25.63, 결과 : 과체중\n",
+      "\n",
+      "하아 69 194\n",
+      "\n",
+      " 이름 : 하아, 몸무게 : 69kg, 키 : 194cm, bmi 지수 : 18.33, 결과 : 저체중\n",
+      "\n",
+      "하가 94 185\n",
+      "\n",
+      " 이름 : 하가, 몸무게 : 94kg, 키 : 185cm, bmi 지수 : 27.47, 결과 : 과체중\n",
+      "\n",
+      "마가 51 168\n",
+      "\n",
+      " 이름 : 마가, 몸무게 : 51kg, 키 : 168cm, bmi 지수 : 18.07, 결과 : 저체중\n",
+      "\n",
+      "가파 48 150\n",
+      "\n",
+      " 이름 : 가파, 몸무게 : 48kg, 키 : 150cm, bmi 지수 : 21.33, 결과 : 정상\n",
+      "\n",
+      "바바 68 191\n",
+      "\n",
+      " 이름 : 바바, 몸무게 : 68kg, 키 : 191cm, bmi 지수 : 18.64, 결과 : 정상\n",
+      "\n",
+      "나아 48 171\n",
+      "\n",
+      " 이름 : 나아, 몸무게 : 48kg, 키 : 171cm, bmi 지수 : 16.42, 결과 : 저체중\n",
+      "\n",
+      "파아 45 140\n",
+      "\n",
+      " 이름 : 파아, 몸무게 : 45kg, 키 : 140cm, bmi 지수 : 22.96, 결과 : 정상\n",
+      "\n",
+      "바하 53 143\n",
+      "\n",
+      " 이름 : 바하, 몸무게 : 53kg, 키 : 143cm, bmi 지수 : 25.92, 결과 : 과체중\n",
+      "\n",
+      "바파 68 154\n",
+      "\n",
+      " 이름 : 바파, 몸무게 : 68kg, 키 : 154cm, bmi 지수 : 28.67, 결과 : 과체중\n",
+      "\n",
+      "차마 88 193\n",
+      "\n",
+      " 이름 : 차마, 몸무게 : 88kg, 키 : 193cm, bmi 지수 : 23.62, 결과 : 정상\n",
+      "\n",
+      "다타 88 167\n",
+      "\n",
+      " 이름 : 다타, 몸무게 : 88kg, 키 : 167cm, bmi 지수 : 31.55, 결과 : 과체중\n",
+      "\n",
+      "차나 67 200\n",
+      "\n",
+      " 이름 : 차나, 몸무게 : 67kg, 키 : 200cm, bmi 지수 : 16.75, 결과 : 저체중\n",
+      "\n",
+      "라바 76 185\n",
+      "\n",
+      " 이름 : 라바, 몸무게 : 76kg, 키 : 185cm, bmi 지수 : 22.21, 결과 : 정상\n",
+      "\n",
+      "바다 44 175\n",
+      "\n",
+      " 이름 : 바다, 몸무게 : 44kg, 키 : 175cm, bmi 지수 : 14.37, 결과 : 저체중\n",
+      "\n",
+      "가마 64 166\n",
+      "\n",
+      " 이름 : 가마, 몸무게 : 64kg, 키 : 166cm, bmi 지수 : 23.23, 결과 : 정상\n",
+      "\n",
+      "바차 60 159\n",
+      "\n",
+      " 이름 : 바차, 몸무게 : 60kg, 키 : 159cm, bmi 지수 : 23.73, 결과 : 정상\n",
+      "\n",
+      "카라 88 150\n",
+      "\n",
+      " 이름 : 카라, 몸무게 : 88kg, 키 : 150cm, bmi 지수 : 39.11, 결과 : 과체중\n",
+      "\n",
+      "자가 49 146\n",
+      "\n",
+      " 이름 : 자가, 몸무게 : 49kg, 키 : 146cm, bmi 지수 : 22.99, 결과 : 정상\n",
+      "\n",
+      "자가 75 165\n",
+      "\n",
+      " 이름 : 자가, 몸무게 : 75kg, 키 : 165cm, bmi 지수 : 27.55, 결과 : 과체중\n",
+      "\n",
+      "가아 75 144\n",
+      "\n",
+      " 이름 : 가아, 몸무게 : 75kg, 키 : 144cm, bmi 지수 : 36.17, 결과 : 과체중\n",
+      "\n",
+      "자라 75 143\n",
+      "\n",
+      " 이름 : 자라, 몸무게 : 75kg, 키 : 143cm, bmi 지수 : 36.68, 결과 : 과체중\n",
+      "\n",
+      "마자 41 167\n",
+      "\n",
+      " 이름 : 마자, 몸무게 : 41kg, 키 : 167cm, bmi 지수 : 14.70, 결과 : 저체중\n",
+      "\n",
+      "가차 81 160\n",
+      "\n",
+      " 이름 : 가차, 몸무게 : 81kg, 키 : 160cm, bmi 지수 : 31.64, 결과 : 과체중\n",
+      "\n",
+      "다바 62 167\n",
+      "\n",
+      " 이름 : 다바, 몸무게 : 62kg, 키 : 167cm, bmi 지수 : 22.23, 결과 : 정상\n",
+      "\n",
+      "카바 71 168\n",
+      "\n",
+      " 이름 : 카바, 몸무게 : 71kg, 키 : 168cm, bmi 지수 : 25.16, 결과 : 과체중\n",
+      "\n",
+      "타타 55 197\n",
+      "\n",
+      " 이름 : 타타, 몸무게 : 55kg, 키 : 197cm, bmi 지수 : 14.17, 결과 : 저체중\n",
+      "\n",
+      "카자 44 200\n",
+      "\n",
+      " 이름 : 카자, 몸무게 : 44kg, 키 : 200cm, bmi 지수 : 11.00, 결과 : 저체중\n",
+      "\n",
+      "아아 52 188\n",
+      "\n",
+      " 이름 : 아아, 몸무게 : 52kg, 키 : 188cm, bmi 지수 : 14.71, 결과 : 저체중\n",
+      "\n",
+      "사사 59 155\n",
+      "\n",
+      " 이름 : 사사, 몸무게 : 59kg, 키 : 155cm, bmi 지수 : 24.56, 결과 : 정상\n",
+      "\n",
+      "가라 71 163\n",
+      "\n",
+      " 이름 : 가라, 몸무게 : 71kg, 키 : 163cm, bmi 지수 : 26.72, 결과 : 과체중\n",
+      "\n",
+      "바사 44 189\n",
+      "\n",
+      " 이름 : 바사, 몸무게 : 44kg, 키 : 189cm, bmi 지수 : 12.32, 결과 : 저체중\n",
+      "\n",
+      "가차 84 145\n",
+      "\n",
+      " 이름 : 가차, 몸무게 : 84kg, 키 : 145cm, bmi 지수 : 39.95, 결과 : 과체중\n",
+      "\n",
+      "나자 67 190\n",
+      "\n",
+      " 이름 : 나자, 몸무게 : 67kg, 키 : 190cm, bmi 지수 : 18.56, 결과 : 정상\n",
+      "\n",
+      "차마 89 148\n",
+      "\n",
+      " 이름 : 차마, 몸무게 : 89kg, 키 : 148cm, bmi 지수 : 40.63, 결과 : 과체중\n",
+      "\n",
+      "하마 89 183\n",
+      "\n",
+      " 이름 : 하마, 몸무게 : 89kg, 키 : 183cm, bmi 지수 : 26.58, 결과 : 과체중\n",
+      "\n",
+      "다사 71 176\n",
+      "\n",
+      " 이름 : 다사, 몸무게 : 71kg, 키 : 176cm, bmi 지수 : 22.92, 결과 : 정상\n",
+      "\n",
+      "차가 97 200\n",
+      "\n",
+      " 이름 : 차가, 몸무게 : 97kg, 키 : 200cm, bmi 지수 : 24.25, 결과 : 정상\n",
+      "\n",
+      "다하 87 198\n",
+      "\n",
+      " 이름 : 다하, 몸무게 : 87kg, 키 : 198cm, bmi 지수 : 22.19, 결과 : 정상\n",
+      "\n",
+      "아가 56 170\n",
+      "\n",
+      " 이름 : 아가, 몸무게 : 56kg, 키 : 170cm, bmi 지수 : 19.38, 결과 : 정상\n",
+      "\n",
+      "사자 55 160\n",
+      "\n",
+      " 이름 : 사자, 몸무게 : 55kg, 키 : 160cm, bmi 지수 : 21.48, 결과 : 정상\n",
+      "\n",
+      "카자 77 169\n",
+      "\n",
+      " 이름 : 카자, 몸무게 : 77kg, 키 : 169cm, bmi 지수 : 26.96, 결과 : 과체중\n",
+      "\n",
+      "파하 53 168\n",
+      "\n",
+      " 이름 : 파하, 몸무게 : 53kg, 키 : 168cm, bmi 지수 : 18.78, 결과 : 정상\n",
+      "\n",
+      "아사 95 172\n",
+      "\n",
+      " 이름 : 아사, 몸무게 : 95kg, 키 : 172cm, bmi 지수 : 32.11, 결과 : 과체중\n",
+      "\n",
+      "차타 81 178\n",
+      "\n",
+      " 이름 : 차타, 몸무게 : 81kg, 키 : 178cm, bmi 지수 : 25.56, 결과 : 과체중\n",
+      "\n",
+      "사차 42 145\n",
+      "\n",
+      " 이름 : 사차, 몸무게 : 42kg, 키 : 145cm, bmi 지수 : 19.98, 결과 : 정상\n",
+      "\n",
+      "나라 52 143\n",
+      "\n",
+      " 이름 : 나라, 몸무게 : 52kg, 키 : 143cm, bmi 지수 : 25.43, 결과 : 과체중\n",
+      "\n",
+      "자아 45 145\n",
+      "\n",
+      " 이름 : 자아, 몸무게 : 45kg, 키 : 145cm, bmi 지수 : 21.40, 결과 : 정상\n",
+      "\n",
+      "다사 85 186\n",
+      "\n",
+      " 이름 : 다사, 몸무게 : 85kg, 키 : 186cm, bmi 지수 : 24.57, 결과 : 정상\n",
+      "\n",
+      "아자 77 151\n",
+      "\n",
+      " 이름 : 아자, 몸무게 : 77kg, 키 : 151cm, bmi 지수 : 33.77, 결과 : 과체중\n",
+      "\n",
+      "타카 90 190\n",
+      "\n",
+      " 이름 : 타카, 몸무게 : 90kg, 키 : 190cm, bmi 지수 : 24.93, 결과 : 정상\n",
+      "\n",
+      "아나 59 152\n",
+      "\n",
+      " 이름 : 아나, 몸무게 : 59kg, 키 : 152cm, bmi 지수 : 25.54, 결과 : 과체중\n",
+      "\n",
+      "라타 40 149\n",
+      "\n",
+      " 이름 : 라타, 몸무게 : 40kg, 키 : 149cm, bmi 지수 : 18.02, 결과 : 저체중\n",
+      "\n",
+      "하타 64 168\n",
+      "\n",
+      " 이름 : 하타, 몸무게 : 64kg, 키 : 168cm, bmi 지수 : 22.68, 결과 : 정상\n",
+      "\n",
+      "하나 92 171\n",
+      "\n",
+      " 이름 : 하나, 몸무게 : 92kg, 키 : 171cm, bmi 지수 : 31.46, 결과 : 과체중\n",
+      "\n",
+      "자타 56 198\n",
+      "\n",
+      " 이름 : 자타, 몸무게 : 56kg, 키 : 198cm, bmi 지수 : 14.28, 결과 : 저체중\n",
+      "\n",
+      "사타 80 192\n",
+      "\n",
+      " 이름 : 사타, 몸무게 : 80kg, 키 : 192cm, bmi 지수 : 21.70, 결과 : 정상\n",
+      "\n",
+      "라다 68 193\n",
+      "\n",
+      " 이름 : 라다, 몸무게 : 68kg, 키 : 193cm, bmi 지수 : 18.26, 결과 : 저체중\n",
+      "\n",
+      "타다 51 154\n",
+      "\n",
+      " 이름 : 타다, 몸무게 : 51kg, 키 : 154cm, bmi 지수 : 21.50, 결과 : 정상\n",
+      "\n",
+      "차가 57 193\n",
+      "\n",
+      " 이름 : 차가, 몸무게 : 57kg, 키 : 193cm, bmi 지수 : 15.30, 결과 : 저체중\n",
+      "\n",
+      "라카 70 171\n",
+      "\n",
+      " 이름 : 라카, 몸무게 : 70kg, 키 : 171cm, bmi 지수 : 23.94, 결과 : 정상\n",
+      "\n",
+      "파나 87 143\n",
+      "\n",
+      " 이름 : 파나, 몸무게 : 87kg, 키 : 143cm, bmi 지수 : 42.54, 결과 : 과체중\n",
+      "\n",
+      "하마 92 153\n",
+      "\n",
+      " 이름 : 하마, 몸무게 : 92kg, 키 : 153cm, bmi 지수 : 39.30, 결과 : 과체중\n",
+      "\n",
+      "마바 52 198\n",
+      "\n",
+      " 이름 : 마바, 몸무게 : 52kg, 키 : 198cm, bmi 지수 : 13.26, 결과 : 저체중\n",
+      "\n",
+      "카아 73 185\n",
+      "\n",
+      " 이름 : 카아, 몸무게 : 73kg, 키 : 185cm, bmi 지수 : 21.33, 결과 : 정상\n",
+      "\n",
+      "아라 87 176\n",
+      "\n",
+      " 이름 : 아라, 몸무게 : 87kg, 키 : 176cm, bmi 지수 : 28.09, 결과 : 과체중\n",
+      "\n",
+      "다파 71 182\n",
+      "\n",
+      " 이름 : 다파, 몸무게 : 71kg, 키 : 182cm, bmi 지수 : 21.43, 결과 : 정상\n",
+      "\n",
+      "라자 55 166\n",
+      "\n",
+      " 이름 : 라자, 몸무게 : 55kg, 키 : 166cm, bmi 지수 : 19.96, 결과 : 정상\n",
+      "\n",
+      "마가 84 153\n",
+      "\n",
+      " 이름 : 마가, 몸무게 : 84kg, 키 : 153cm, bmi 지수 : 35.88, 결과 : 과체중\n",
+      "\n",
+      "자사 79 195\n",
+      "\n",
+      " 이름 : 자사, 몸무게 : 79kg, 키 : 195cm, bmi 지수 : 20.78, 결과 : 정상\n",
+      "\n",
+      "나타 63 160\n",
+      "\n",
+      " 이름 : 나타, 몸무게 : 63kg, 키 : 160cm, bmi 지수 : 24.61, 결과 : 정상\n",
+      "\n",
+      "라사 52 194\n",
+      "\n",
+      " 이름 : 라사, 몸무게 : 52kg, 키 : 194cm, bmi 지수 : 13.82, 결과 : 저체중\n",
+      "\n",
+      "타마 62 185\n",
+      "\n",
+      " 이름 : 타마, 몸무게 : 62kg, 키 : 185cm, bmi 지수 : 18.12, 결과 : 저체중\n",
+      "\n",
+      "하자 93 152\n",
+      "\n",
+      " 이름 : 하자, 몸무게 : 93kg, 키 : 152cm, bmi 지수 : 40.25, 결과 : 과체중\n",
+      "\n",
+      "바차 50 161\n",
+      "\n",
+      " 이름 : 바차, 몸무게 : 50kg, 키 : 161cm, bmi 지수 : 19.29, 결과 : 정상\n",
+      "\n",
+      "사자 99 141\n",
+      "\n",
+      " 이름 : 사자, 몸무게 : 99kg, 키 : 141cm, bmi 지수 : 49.80, 결과 : 과체중\n",
+      "\n",
+      "바가 72 182\n",
+      "\n",
+      " 이름 : 바가, 몸무게 : 72kg, 키 : 182cm, bmi 지수 : 21.74, 결과 : 정상\n",
+      "\n",
+      "바나 88 181\n",
+      "\n",
+      " 이름 : 바나, 몸무게 : 88kg, 키 : 181cm, bmi 지수 : 26.86, 결과 : 과체중\n",
+      "\n",
+      "차파 95 143\n",
+      "\n",
+      " 이름 : 차파, 몸무게 : 95kg, 키 : 143cm, bmi 지수 : 46.46, 결과 : 과체중\n",
+      "\n",
+      "차다 69 145\n",
+      "\n",
+      " 이름 : 차다, 몸무게 : 69kg, 키 : 145cm, bmi 지수 : 32.82, 결과 : 과체중\n",
+      "\n",
+      "라가 92 178\n",
+      "\n",
+      " 이름 : 라가, 몸무게 : 92kg, 키 : 178cm, bmi 지수 : 29.04, 결과 : 과체중\n",
+      "\n",
+      "타파 97 180\n",
+      "\n",
+      " 이름 : 타파, 몸무게 : 97kg, 키 : 180cm, bmi 지수 : 29.94, 결과 : 과체중\n",
+      "\n",
+      "마파 99 183\n",
+      "\n",
+      " 이름 : 마파, 몸무게 : 99kg, 키 : 183cm, bmi 지수 : 29.56, 결과 : 과체중\n",
+      "\n",
+      "바타 97 146\n",
+      "\n",
+      " 이름 : 바타, 몸무게 : 97kg, 키 : 146cm, bmi 지수 : 45.51, 결과 : 과체중\n",
+      "\n",
+      "라타 74 143\n",
+      "\n",
+      " 이름 : 라타, 몸무게 : 74kg, 키 : 143cm, bmi 지수 : 36.19, 결과 : 과체중\n",
+      "\n",
+      "바아 40 162\n",
+      "\n",
+      " 이름 : 바아, 몸무게 : 40kg, 키 : 162cm, bmi 지수 : 15.24, 결과 : 저체중\n",
+      "\n",
+      "사타 66 158\n",
+      "\n",
+      " 이름 : 사타, 몸무게 : 66kg, 키 : 158cm, bmi 지수 : 26.44, 결과 : 과체중\n",
+      "\n",
+      "하자 92 182\n",
+      "\n",
+      " 이름 : 하자, 몸무게 : 92kg, 키 : 182cm, bmi 지수 : 27.77, 결과 : 과체중\n",
+      "\n",
+      "차바 93 171\n",
+      "\n",
+      " 이름 : 차바, 몸무게 : 93kg, 키 : 171cm, bmi 지수 : 31.80, 결과 : 과체중\n",
+      "\n",
+      "아하 42 177\n",
+      "\n",
+      " 이름 : 아하, 몸무게 : 42kg, 키 : 177cm, bmi 지수 : 13.41, 결과 : 저체중\n",
+      "\n",
+      "다타 71 190\n",
+      "\n",
+      " 이름 : 다타, 몸무게 : 71kg, 키 : 190cm, bmi 지수 : 19.67, 결과 : 정상\n",
+      "\n",
+      "바가 72 186\n",
+      "\n",
+      " 이름 : 바가, 몸무게 : 72kg, 키 : 186cm, bmi 지수 : 20.81, 결과 : 정상\n",
+      "\n",
+      "마다 52 146\n",
+      "\n",
+      " 이름 : 마다, 몸무게 : 52kg, 키 : 146cm, bmi 지수 : 24.39, 결과 : 정상\n",
+      "\n",
+      "자다 88 181\n",
+      "\n",
+      " 이름 : 자다, 몸무게 : 88kg, 키 : 181cm, bmi 지수 : 26.86, 결과 : 과체중\n",
+      "\n",
+      "라다 48 148\n",
+      "\n",
+      " 이름 : 라다, 몸무게 : 48kg, 키 : 148cm, bmi 지수 : 21.91, 결과 : 정상\n",
+      "\n",
+      "타마 40 152\n",
+      "\n",
+      " 이름 : 타마, 몸무게 : 40kg, 키 : 152cm, bmi 지수 : 17.31, 결과 : 저체중\n",
+      "\n",
+      "파타 67 151\n",
+      "\n",
+      " 이름 : 파타, 몸무게 : 67kg, 키 : 151cm, bmi 지수 : 29.38, 결과 : 과체중\n",
+      "\n",
+      "바차 70 158\n",
+      "\n",
+      " 이름 : 바차, 몸무게 : 70kg, 키 : 158cm, bmi 지수 : 28.04, 결과 : 과체중\n",
+      "\n",
+      "차자 92 172\n",
+      "\n",
+      " 이름 : 차자, 몸무게 : 92kg, 키 : 172cm, bmi 지수 : 31.10, 결과 : 과체중\n",
+      "\n",
+      "라마 96 153\n",
+      "\n",
+      " 이름 : 라마, 몸무게 : 96kg, 키 : 153cm, bmi 지수 : 41.01, 결과 : 과체중\n",
+      "\n",
+      "하나 54 141\n",
+      "\n",
+      " 이름 : 하나, 몸무게 : 54kg, 키 : 141cm, bmi 지수 : 27.16, 결과 : 과체중\n",
+      "\n",
+      "라라 60 167\n",
+      "\n",
+      " 이름 : 라라, 몸무게 : 60kg, 키 : 167cm, bmi 지수 : 21.51, 결과 : 정상\n",
+      "\n",
+      "하다 54 164\n",
+      "\n",
+      " 이름 : 하다, 몸무게 : 54kg, 키 : 164cm, bmi 지수 : 20.08, 결과 : 정상\n",
+      "\n",
+      "라파 79 155\n",
+      "\n",
+      " 이름 : 라파, 몸무게 : 79kg, 키 : 155cm, bmi 지수 : 32.88, 결과 : 과체중\n",
+      "\n",
+      "파다 47 184\n",
+      "\n",
+      " 이름 : 파다, 몸무게 : 47kg, 키 : 184cm, bmi 지수 : 13.88, 결과 : 저체중\n",
+      "\n",
+      "사하 92 186\n",
+      "\n",
+      " 이름 : 사하, 몸무게 : 92kg, 키 : 186cm, bmi 지수 : 26.59, 결과 : 과체중\n",
+      "\n",
+      "다나 99 173\n",
+      "\n",
+      " 이름 : 다나, 몸무게 : 99kg, 키 : 173cm, bmi 지수 : 33.08, 결과 : 과체중\n",
+      "\n",
+      "사타 43 188\n",
+      "\n",
+      " 이름 : 사타, 몸무게 : 43kg, 키 : 188cm, bmi 지수 : 12.17, 결과 : 저체중\n",
+      "\n",
+      "타하 88 174\n",
+      "\n",
+      " 이름 : 타하, 몸무게 : 88kg, 키 : 174cm, bmi 지수 : 29.07, 결과 : 과체중\n",
+      "\n",
+      "바바 70 175\n",
+      "\n",
+      " 이름 : 바바, 몸무게 : 70kg, 키 : 175cm, bmi 지수 : 22.86, 결과 : 정상\n",
+      "\n",
+      "아가 80 155\n",
+      "\n",
+      " 이름 : 아가, 몸무게 : 80kg, 키 : 155cm, bmi 지수 : 33.30, 결과 : 과체중\n",
+      "\n",
+      "사카 72 186\n",
+      "\n",
+      " 이름 : 사카, 몸무게 : 72kg, 키 : 186cm, bmi 지수 : 20.81, 결과 : 정상\n",
+      "\n",
+      "라타 51 141\n",
+      "\n",
+      " 이름 : 라타, 몸무게 : 51kg, 키 : 141cm, bmi 지수 : 25.65, 결과 : 과체중\n",
+      "\n",
+      "차마 74 144\n",
+      "\n",
+      " 이름 : 차마, 몸무게 : 74kg, 키 : 144cm, bmi 지수 : 35.69, 결과 : 과체중\n",
+      "\n",
+      "라자 98 197\n",
+      "\n",
+      " 이름 : 라자, 몸무게 : 98kg, 키 : 197cm, bmi 지수 : 25.25, 결과 : 과체중\n",
+      "\n",
+      "차가 71 167\n",
+      "\n",
+      " 이름 : 차가, 몸무게 : 71kg, 키 : 167cm, bmi 지수 : 25.46, 결과 : 과체중\n",
+      "\n",
+      "가파 73 187\n",
+      "\n",
+      " 이름 : 가파, 몸무게 : 73kg, 키 : 187cm, bmi 지수 : 20.88, 결과 : 정상\n",
+      "\n",
+      "마마 79 146\n",
+      "\n",
+      " 이름 : 마마, 몸무게 : 79kg, 키 : 146cm, bmi 지수 : 37.06, 결과 : 과체중\n",
+      "\n",
+      "아아 93 142\n",
+      "\n",
+      " 이름 : 아아, 몸무게 : 93kg, 키 : 142cm, bmi 지수 : 46.12, 결과 : 과체중\n",
+      "\n",
+      "마하 53 164\n",
+      "\n",
+      " 이름 : 마하, 몸무게 : 53kg, 키 : 164cm, bmi 지수 : 19.71, 결과 : 정상\n",
+      "\n",
+      "가아 42 169\n",
+      "\n",
+      " 이름 : 가아, 몸무게 : 42kg, 키 : 169cm, bmi 지수 : 14.71, 결과 : 저체중\n",
+      "\n",
+      "가차 62 142\n",
+      "\n",
+      " 이름 : 가차, 몸무게 : 62kg, 키 : 142cm, bmi 지수 : 30.75, 결과 : 과체중\n",
+      "\n",
+      "바가 80 158\n",
+      "\n",
+      " 이름 : 바가, 몸무게 : 80kg, 키 : 158cm, bmi 지수 : 32.05, 결과 : 과체중\n",
+      "\n",
+      "바자 99 149\n",
+      "\n",
+      " 이름 : 바자, 몸무게 : 99kg, 키 : 149cm, bmi 지수 : 44.59, 결과 : 과체중\n",
+      "\n",
+      "라바 51 159\n",
+      "\n",
+      " 이름 : 라바, 몸무게 : 51kg, 키 : 159cm, bmi 지수 : 20.17, 결과 : 정상\n",
+      "\n",
+      "라아 69 143\n",
+      "\n",
+      " 이름 : 라아, 몸무게 : 69kg, 키 : 143cm, bmi 지수 : 33.74, 결과 : 과체중\n",
+      "\n",
+      "타바 80 140\n",
+      "\n",
+      " 이름 : 타바, 몸무게 : 80kg, 키 : 140cm, bmi 지수 : 40.82, 결과 : 과체중\n",
+      "\n",
+      "자하 76 186\n",
+      "\n",
+      " 이름 : 자하, 몸무게 : 76kg, 키 : 186cm, bmi 지수 : 21.97, 결과 : 정상\n",
+      "\n",
+      "바나 62 171\n",
+      "\n",
+      " 이름 : 바나, 몸무게 : 62kg, 키 : 171cm, bmi 지수 : 21.20, 결과 : 정상\n",
+      "\n",
+      "바아 82 167\n",
+      "\n",
+      " 이름 : 바아, 몸무게 : 82kg, 키 : 167cm, bmi 지수 : 29.40, 결과 : 과체중\n",
+      "\n",
+      "하하 96 143\n",
+      "\n",
+      " 이름 : 하하, 몸무게 : 96kg, 키 : 143cm, bmi 지수 : 46.95, 결과 : 과체중\n",
+      "\n",
+      "아파 53 145\n",
+      "\n",
+      " 이름 : 아파, 몸무게 : 53kg, 키 : 145cm, bmi 지수 : 25.21, 결과 : 과체중\n",
+      "\n",
+      "타차 93 156\n",
+      "\n",
+      " 이름 : 타차, 몸무게 : 93kg, 키 : 156cm, bmi 지수 : 38.21, 결과 : 과체중\n",
+      "\n",
+      "가다 41 171\n",
+      "\n",
+      " 이름 : 가다, 몸무게 : 41kg, 키 : 171cm, bmi 지수 : 14.02, 결과 : 저체중\n",
+      "\n",
+      "바사 43 159\n",
+      "\n",
+      " 이름 : 바사, 몸무게 : 43kg, 키 : 159cm, bmi 지수 : 17.01, 결과 : 저체중\n",
+      "\n",
+      "자라 57 181\n",
+      "\n",
+      " 이름 : 자라, 몸무게 : 57kg, 키 : 181cm, bmi 지수 : 17.40, 결과 : 저체중\n",
+      "\n",
+      "다다 67 172\n",
+      "\n",
+      " 이름 : 다다, 몸무게 : 67kg, 키 : 172cm, bmi 지수 : 22.65, 결과 : 정상\n",
+      "\n",
+      "라카 67 162\n",
+      "\n",
+      " 이름 : 라카, 몸무게 : 67kg, 키 : 162cm, bmi 지수 : 25.53, 결과 : 과체중\n",
+      "\n",
+      "다마 71 156\n",
+      "\n",
+      " 이름 : 다마, 몸무게 : 71kg, 키 : 156cm, bmi 지수 : 29.17, 결과 : 과체중\n",
+      "\n",
+      "사카 48 186\n",
+      "\n",
+      " 이름 : 사카, 몸무게 : 48kg, 키 : 186cm, bmi 지수 : 13.87, 결과 : 저체중\n",
+      "\n",
+      "가가 58 153\n",
+      "\n",
+      " 이름 : 가가, 몸무게 : 58kg, 키 : 153cm, bmi 지수 : 24.78, 결과 : 정상\n",
+      "\n",
+      "라라 54 166\n",
+      "\n",
+      " 이름 : 라라, 몸무게 : 54kg, 키 : 166cm, bmi 지수 : 19.60, 결과 : 정상\n",
+      "\n",
+      "아타 59 171\n",
+      "\n",
+      " 이름 : 아타, 몸무게 : 59kg, 키 : 171cm, bmi 지수 : 20.18, 결과 : 정상\n",
+      "\n",
+      "바라 61 174\n",
+      "\n",
+      " 이름 : 바라, 몸무게 : 61kg, 키 : 174cm, bmi 지수 : 20.15, 결과 : 정상\n",
+      "\n",
+      "자마 75 195\n",
+      "\n",
+      " 이름 : 자마, 몸무게 : 75kg, 키 : 195cm, bmi 지수 : 19.72, 결과 : 정상\n",
+      "\n",
+      "사타 82 166\n",
+      "\n",
+      " 이름 : 사타, 몸무게 : 82kg, 키 : 166cm, bmi 지수 : 29.76, 결과 : 과체중\n",
+      "\n",
+      "라라 74 164\n",
+      "\n",
+      " 이름 : 라라, 몸무게 : 74kg, 키 : 164cm, bmi 지수 : 27.51, 결과 : 과체중\n",
+      "\n",
+      "하바 47 183\n",
+      "\n",
+      " 이름 : 하바, 몸무게 : 47kg, 키 : 183cm, bmi 지수 : 14.03, 결과 : 저체중\n",
+      "\n",
+      "나마 46 179\n",
+      "\n",
+      " 이름 : 나마, 몸무게 : 46kg, 키 : 179cm, bmi 지수 : 14.36, 결과 : 저체중\n",
+      "\n",
+      "라사 40 190\n",
+      "\n",
+      " 이름 : 라사, 몸무게 : 40kg, 키 : 190cm, bmi 지수 : 11.08, 결과 : 저체중\n",
+      "\n",
+      "마나 77 181\n",
+      "\n",
+      " 이름 : 마나, 몸무게 : 77kg, 키 : 181cm, bmi 지수 : 23.50, 결과 : 정상\n",
+      "\n",
+      "가가 52 145\n",
+      "\n",
+      " 이름 : 가가, 몸무게 : 52kg, 키 : 145cm, bmi 지수 : 24.73, 결과 : 정상\n",
+      "\n",
+      "자차 42 156\n",
+      "\n",
+      " 이름 : 자차, 몸무게 : 42kg, 키 : 156cm, bmi 지수 : 17.26, 결과 : 저체중\n",
+      "\n",
+      "차나 76 157\n",
+      "\n",
+      " 이름 : 차나, 몸무게 : 76kg, 키 : 157cm, bmi 지수 : 30.83, 결과 : 과체중\n",
+      "\n",
+      "자다 73 192\n",
+      "\n",
+      " 이름 : 자다, 몸무게 : 73kg, 키 : 192cm, bmi 지수 : 19.80, 결과 : 정상\n",
+      "\n",
+      "라다 93 194\n",
+      "\n",
+      " 이름 : 라다, 몸무게 : 93kg, 키 : 194cm, bmi 지수 : 24.71, 결과 : 정상\n",
+      "\n",
+      "나자 98 199\n",
+      "\n",
+      " 이름 : 나자, 몸무게 : 98kg, 키 : 199cm, bmi 지수 : 24.75, 결과 : 정상\n",
+      "\n",
+      "아카 84 194\n",
+      "\n",
+      " 이름 : 아카, 몸무게 : 84kg, 키 : 194cm, bmi 지수 : 22.32, 결과 : 정상\n",
+      "\n",
+      "나파 52 144\n",
+      "\n",
+      " 이름 : 나파, 몸무게 : 52kg, 키 : 144cm, bmi 지수 : 25.08, 결과 : 과체중\n",
+      "\n",
+      "타차 82 191\n",
+      "\n",
+      " 이름 : 타차, 몸무게 : 82kg, 키 : 191cm, bmi 지수 : 22.48, 결과 : 정상\n",
+      "\n",
+      "가나 75 196\n",
+      "\n",
+      " 이름 : 가나, 몸무게 : 75kg, 키 : 196cm, bmi 지수 : 19.52, 결과 : 정상\n",
+      "\n",
+      "라다 48 170\n",
+      "\n",
+      " 이름 : 라다, 몸무게 : 48kg, 키 : 170cm, bmi 지수 : 16.61, 결과 : 저체중\n",
+      "\n",
+      "자자 93 193\n",
+      "\n",
+      " 이름 : 자자, 몸무게 : 93kg, 키 : 193cm, bmi 지수 : 24.97, 결과 : 정상\n",
+      "\n",
+      "타다 48 196\n",
+      "\n",
+      " 이름 : 타다, 몸무게 : 48kg, 키 : 196cm, bmi 지수 : 12.49, 결과 : 저체중\n",
+      "\n",
+      "차가 83 197\n",
+      "\n",
+      " 이름 : 차가, 몸무게 : 83kg, 키 : 197cm, bmi 지수 : 21.39, 결과 : 정상\n",
+      "\n",
+      "자하 76 165\n",
+      "\n",
+      " 이름 : 자하, 몸무게 : 76kg, 키 : 165cm, bmi 지수 : 27.92, 결과 : 과체중\n",
+      "\n",
+      "하자 61 142\n",
+      "\n",
+      " 이름 : 하자, 몸무게 : 61kg, 키 : 142cm, bmi 지수 : 30.25, 결과 : 과체중\n",
+      "\n",
+      "하타 74 199\n",
+      "\n",
+      " 이름 : 하타, 몸무게 : 74kg, 키 : 199cm, bmi 지수 : 18.69, 결과 : 정상\n",
+      "\n",
+      "타다 83 141\n",
+      "\n",
+      " 이름 : 타다, 몸무게 : 83kg, 키 : 141cm, bmi 지수 : 41.75, 결과 : 과체중\n",
+      "\n",
+      "하카 58 150\n",
+      "\n",
+      " 이름 : 하카, 몸무게 : 58kg, 키 : 150cm, bmi 지수 : 25.78, 결과 : 과체중\n",
+      "\n",
+      "자차 72 157\n",
+      "\n",
+      " 이름 : 자차, 몸무게 : 72kg, 키 : 157cm, bmi 지수 : 29.21, 결과 : 과체중\n",
+      "\n",
+      "하가 47 156\n",
+      "\n",
+      " 이름 : 하가, 몸무게 : 47kg, 키 : 156cm, bmi 지수 : 19.31, 결과 : 정상\n",
+      "\n",
+      "마나 64 141\n",
+      "\n",
+      " 이름 : 마나, 몸무게 : 64kg, 키 : 141cm, bmi 지수 : 32.19, 결과 : 과체중\n",
+      "\n",
+      "아파 65 162\n",
+      "\n",
+      " 이름 : 아파, 몸무게 : 65kg, 키 : 162cm, bmi 지수 : 24.77, 결과 : 정상\n",
+      "\n",
+      "다아 94 185\n",
+      "\n",
+      " 이름 : 다아, 몸무게 : 94kg, 키 : 185cm, bmi 지수 : 27.47, 결과 : 과체중\n",
+      "\n",
+      "다아 68 164\n",
+      "\n",
+      " 이름 : 다아, 몸무게 : 68kg, 키 : 164cm, bmi 지수 : 25.28, 결과 : 과체중\n",
+      "\n",
+      "타파 62 147\n",
+      "\n",
+      " 이름 : 타파, 몸무게 : 62kg, 키 : 147cm, bmi 지수 : 28.69, 결과 : 과체중\n",
+      "\n",
+      "카바 57 183\n",
+      "\n",
+      " 이름 : 카바, 몸무게 : 57kg, 키 : 183cm, bmi 지수 : 17.02, 결과 : 저체중\n",
+      "\n",
+      "하하 84 151\n",
+      "\n",
+      " 이름 : 하하, 몸무게 : 84kg, 키 : 151cm, bmi 지수 : 36.84, 결과 : 과체중\n",
+      "\n",
+      "바하 92 140\n",
+      "\n",
+      " 이름 : 바하, 몸무게 : 92kg, 키 : 140cm, bmi 지수 : 46.94, 결과 : 과체중\n",
+      "\n",
+      "자마 80 183\n",
+      "\n",
+      " 이름 : 자마, 몸무게 : 80kg, 키 : 183cm, bmi 지수 : 23.89, 결과 : 정상\n",
+      "\n",
+      "카나 59 193\n",
+      "\n",
+      " 이름 : 카나, 몸무게 : 59kg, 키 : 193cm, bmi 지수 : 15.84, 결과 : 저체중\n",
+      "\n",
+      "사자 86 146\n",
+      "\n",
+      " 이름 : 사자, 몸무게 : 86kg, 키 : 146cm, bmi 지수 : 40.35, 결과 : 과체중\n",
+      "\n",
+      "나차 88 168\n",
+      "\n",
+      " 이름 : 나차, 몸무게 : 88kg, 키 : 168cm, bmi 지수 : 31.18, 결과 : 과체중\n",
+      "\n",
+      "다마 43 161\n",
+      "\n",
+      " 이름 : 다마, 몸무게 : 43kg, 키 : 161cm, bmi 지수 : 16.59, 결과 : 저체중\n",
+      "\n",
+      "사타 86 149\n",
+      "\n",
+      " 이름 : 사타, 몸무게 : 86kg, 키 : 149cm, bmi 지수 : 38.74, 결과 : 과체중\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#info.txt 읽어서 bmi 지수 계산\n",
+    "# 하사 93 154\n",
+    "# 이름 : 하사\n",
+    "# 몸무게 : 93kg\n",
+    "# 키 : 154cm\n",
+    "# bmi 지수 : 39.2 ( weight / (height/100) ** 2 )\n",
+    "# 결과 : 고도 비만\n",
+    "\n",
+    "# with open(\"info.txt\", \"r\", encoding=\"utf-8\") as f1:\n",
+    "#     for line in f1:\n",
+    "#         name, weight, height = line.split()\n",
+    "#         weight = int(weight)\n",
+    "#         height = int(height)\n",
+    "#         bmi = weight / (height / 100) ** 2\n",
+    "        \n",
+    "#         if bmi >= 25:\n",
+    "#             result = \"과체중\"\n",
+    "#         elif bmi >= 18.5:\n",
+    "#             result = \"정상\"\n",
+    "#         else:\n",
+    "#             result = \"저체중\"\n",
+    "        \n",
+    "#         print(f\"이름 : {name}, 몸무게 : {weight}kg, 키 : {height}cm, bmi 지수 : {bmi:.2f}, 결과 : {result}\")\n",
+    "\n",
+    "with open(\"info.txt\", \"r\", encoding=\"utf-8\") as f:   \n",
+    "    for info in f:\n",
+    "        # print(info.strip() # 줄바꿈 제거)\n",
+    "        name, weight, height = info.strip().split()\n",
+    "        print(name, weight, height)\n",
+    "        \n",
+    "        bmi = int(weight) / (int(height) / 100) ** 2\n",
+    "        \n",
+    "        if bmi >= 25:\n",
+    "            result = \"과체중\"\n",
+    "        elif bmi >= 18.5:\n",
+    "            result = \"정상\"\n",
+    "        else:\n",
+    "            result = \"저체중\"\n",
+    "        print(\"\\n\", \"이름 : {}, 몸무게 : {}kg, 키 : {}cm, bmi 지수 : {:.2f}, 결과 : {}\".format(name, weight, height, bmi, result))\n",
+    "        print()\n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11e0499a",
+   "metadata": {},
+   "source": [
+    "### 클래스\n",
+    "- 함수, 변수를 포함한 형태\n",
+    "- 객체지향\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "915a56e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 클래스 생성\n",
+    "class Calculator:\n",
+    "    def __init__(self):\n",
+    "        self.result = 0\n",
+    "    \n",
+    "    메서드    \n",
+    "    def add(self, num):\n",
+    "        self.result += num\n",
+    "        return self.result\n",
+    "\n",
+    "    def sub(self, num):\n",
+    "        self.result -= num\n",
+    "        return self.result\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e08c2c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 사용 = 객체 생성\n",
+    "calc = Calculator()\n",
+    "\n",
+    "# 함수 (메서드) 호출\n",
+    "\n",
+    "print(calc.add(10))\n",
+    "\n",
+    "\n",
+    "print(calc.sub(5))\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4a0210b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 클래스 생성\n",
+    "class FourCal:\n",
+    "    def __init__(self, first, second):\n",
+    "        self.first = first\n",
+    "        self.second = second\n",
+    "\n",
+    "    #메서드\n",
+    "    def add(self):\n",
+    "        return self.first + self.second\n",
+    "\n",
+    "    def sub(self):\n",
+    "        return self.first - self.second\n",
+    "    \n",
+    "    def mul(self):\n",
+    "        return self.first * self.second\n",
+    "    \n",
+    "    def div(self):\n",
+    "        return self.first // self.second        \n",
+    "    \n",
+    "    # 주소값 같이 안나오게 하기 위해서 __str__ 메서드 오버라이딩\n",
+    "    def __str__(self):\n",
+    "        return f\"{self.first}, {self.second}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "1be26090",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n",
+      "2\n",
+      "15\n",
+      "1\n",
+      "5, 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# __init__() 메서드는 객체가 생성될 때 자동으로 호출되는 초기화 메서드입니다. 이 메서드는 객체의 속성을 초기화하는 데 사용됩니다. 예를 들어, class FourCal: ...와 같이 클래스가 정의되어 있을 때, calc = FourCal(10, 20)과 같이 객체를 생성하면, _init__() 메서드가 자동으로 호출되어 self.first에 10이, self.second에 20이 할당됩니다. _init__() 메서드는 객체의 초기 상태를 설정하는 데 중요한 역할을 합니다.\n",
+    "calc = FourCal(5, 3)\n",
+    "\n",
+    "print(calc.add()) # TypeError: add() takes 1 positional argument but 2 were given\n",
+    "print(calc.sub())\n",
+    "print(calc.mul())   \n",
+    "print(calc.div())\n",
+    "print(calc) # 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "b2451676",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 상속\n",
+    "\n",
+    "class MoreFourCal(FourCal):\n",
+    "    def pow(self):\n",
+    "        return self.first ** self.second"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "5ee8e1db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n",
+      "125\n"
+     ]
+    }
+   ],
+   "source": [
+    "cale = MoreFourCal(5, 3)\n",
+    "print(cale.add())\n",
+    "print(cale.pow())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b7c780e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 오버라이딩 : 부모 클래스 메서드 재정의\n",
+    "\n",
+    "four1 = FourCal(4, 0)\n",
+    "\n",
+    "#four1.div() # ZeroDivisionError: integer division or modulo by zero\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "b3d9e669",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# super(): 부모\n",
+    "# self : 자식(객체 자신)\n",
+    "\n",
+    "class SafeFourCal(FourCal):\n",
+    "    def div(self):\n",
+    "        if self.second == 0:\n",
+    "            return 0\n",
+    "        else:\n",
+    "            return self.first / self.second\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "e36f0113",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "four1 = SafeFourCal(4, 0)\n",
+    "print(four1.div()) # 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5902fce",
+   "metadata": {},
+   "source": [
+    "### 모듈 : 함수, 변수, 클래스 모아 놓은 파일\n",
+    "\n",
+    "### 패키지 : 모듈의 모임\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "227c8751",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0\n",
+      "3.141592653589793\n",
+      "2\n",
+      "0.8414709848078965\n",
+      "0.5403023058681398\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 파이썬에서 제공하는 기본 모듈\n",
+    "# math가 가지고 있는 모든 설정값들을 메모리에 올림\n",
+    "\n",
+    "import math\n",
+    "\n",
+    "print(math.sqrt(16)) # math 모듈의 sqrt() 함수를 사용하여 16의 제곱근을 계산합니다. math.sqrt() 함수는 math 모듈에서 제공하는 수학 함수 중 하나로, 제곱근을 계산하는 데 사용됩니다. 예를 들어, math.sqrt(16)은 4.0을 반환합니다. math 모듈은 다양한 수학 함수를 제공하며, 이를 활용하여 복잡한 수학 연산을 수행할 수 있습니다.\n",
+    "print(math.pi) # math 모듈의 pi 상수를 출력합니다. math.pi는 원주율을 나타내는 상수로, 약 3.14159의 값을 가지고 있습니다. math 모듈은 다양한 수학 상수와 함수를 제공하며, 이를 활용하여 수학적 계산을 수행할 수 있습니다. math.pi를 사용하면 원주율을 쉽게 참조할 수 있어, 원과 관련된 계산에서 유용하게 사용됩니다.\n",
+    "print(math.floor(2.3)) # math 모듈의 floor() 함수를 사용하여 2.3을 내림하여 정수로 반환합니다. math.floor() 함수는 주어진 숫자를 내림하여 가장 큰 정수로 반환하는 함수입니다. 예를 들어, math.floor(2.3)은 2를 반환합니다. math 모듈은 다양한 수학 함수를 제공하며, 이를 활용하여 수학적 계산을 수행할 수 있습니다. math.floor() 함수를 사용하면 소수점을 내림하여 정수로 변환할 때 유용하게 사용됩니다.\n",
+    "print(math.sin(1)) # math 모듈의 sin() 함수를 사용하여 1의 사인 값을 계산합니다. math.sin() 함수는 주어진 각도의 사인 값을 반환하는 함수입니다. 예를 들어, math.sin(1)은 0.8414709848078965를 반환합니다. math 모듈은 다양한 수학 함수를 제공하며, 이를 활용하여 삼각 함수와 관련된 계산을 수행할 수 있습니다. math.sin() 함수를 사용하면 각도의 사인 값을 쉽게 계산할 수 있어, 삼각 함수와 관련된 문제에서 유용하게 사용됩니다.\n",
+    "print(math.cos(1)) # math 모듈의 cos() 함수를 사용하여 1의 코사인 값을 계산합니다. math.cos() 함수는 주어진 각도의 코사인 값을 반환하는 함수입니다. 예를 들어, math.cos(1)은 0.5403023058681398을 반환합니다. math 모듈은 다양한 수학 함수를 제공하며, 이를 활용하여 삼각 함수와 관련된 계산을 수행할 수 있습니다. math.cos() 함수를 사용하면 각도의 코사인 값을 쉽게 계산할 수 있어, 삼각 함수와 관련된 문제에서 유용하게 사용됩니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "46c2998d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n",
+      "0.8414709848078965\n",
+      "0.5403023058681398\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import ceil, sin, cos, floor\n",
+    "\n",
+    "print(ceil(5.3)) # math 모듈에서 ceil() 함수를 직접 사용하여 5.3을 올림하여 정수로 반환합니다. ceil() 함수는 주어진 숫자를 올림하여 가장 작은 정수로 반환하는 함수입니다. 예를 들어, ceil(5.3)은 6을 반환합니다. math 모듈에서 필요한 함수만을 직접 가져와 사용할 수 있어, 코드의 가독성을 높이고 불필요한 네임스페이스 오염을 방지할 수 있습니다.\n",
+    "print(sin(1)) # math 모듈에서 sin() 함수를 직접 사용하여 1의 사인 값을 계산합니다. sin() 함수는 주어진 각도의 사인 값을 반환하는 함수입니다. 예를 들어, sin(1)은 0.8414709848078965를 반환합니다. math 모듈에서 필요한 함수만을 직접 가져와 사용할 수 있어, 코드의 가독성을 높이고 불필요한 네임스페이스 오염을 방지할 수 있습니다.\n",
+    "print(cos(1)) # math 모듈에서 cos() 함수를 직접 사용하여 1의 코사인 값을 계산합니다. cos() 함수는 주어진 각도의 코사인 값을 반환하는 함수입니다. 예를 들어, cos(1)은 0.5403023058681398을 반환합니다. math 모듈에서 필요한 함수만을 직접 가져와 사용할 수 있어, 코드의 가독성을 높이고 불필요한 네임스페이스 오염을 방지할 수 있습니다.\n",
+    "print(floor(2.3)) # math 모듈에서 floor() 함수를 직접 사용하여 2.3을 내림하여 정수로 반환합니다. floor() 함수는 주어진             숫자를 내림하여 가장 큰 정수로 반환하는 함수입니다. 예를 들어, floor(2.3)은 2를 반환합니다. math 모듈에서 필요한 함수만을 직접 가져와 사용할 수 있어, 코드의 가독성을 높이고 불필요한 네임스페이스 오염을 방지할 수 있습니다 ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "856a93e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# as : 별칭, 모듈이나 함수에 별명을 붙이는 데 사용됩니다. as 키워드를 사용하여 모듈이나 함수를 다른 이름으로 참조할 수 있습니다. 예를 들어, import math as m과 같이 math 모듈을 m이라는 별칭으로 가져올 수 있습니다. 이렇게 하면 m.sqrt(16)과 같이 math 모듈의 sqrt() 함수를 m이라는 별칭을 사용하여 호출할 수 있습니다. as 키워드는 코드의 가독성을 높이고, 긴 모듈 이름을 간단하게 사용할 때 유용하게 사용됩니다.\n",
+    "\n",
+    "from math import ceil as c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "0da783c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3849086839965148\n",
+      "2\n",
+      "3\n",
+      "banana\n",
+      "[4, 3, 1, 5, 2]\n",
+      "[5, 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "# random() : 0.0 <= x < 1.0 임의의 float 숫자\n",
+    "print(random.random()) # random 모듈의 random() 함수를 사용하여 0과 1 사이의 임의의 부동 소수점 숫자를 생성합니다. random.random() 함수는 0.0 이상 1.0 미만의 난수를 반환합니다. 예를 들어, random.random()은 0.123456789와 같은 값을 반환할 수 있습니다. random 모듈은 다양한 난수 생성 함수를 제공하며, 이를 활용하여 무작위 데이터를 생성하거나 시뮬레이션을 수행할 수 있습니다. random.random() 함수를 사용하면 간단하게 난수를 생성할 수 있어, 다양한 응용 분야에서 유용하게 사용됩니다.\n",
+    "print(random.randint(1, 10)) # random 모듈의 randint() 함수를\n",
+    "print(random.randrange(1, 10)) # random 모듈의 randrange() 함수를 사용하여 1과 10 사이의 임의의 정수를 생성합니다. random.randrange(1, 10) 함수는 1 이상 10 미만의 정수를 반환합니다. 예를 들어, random.randrange(1, 10)은 3과 같은 값을 반환할 수 있습니다. random 모듈은 다양한 난수 생성 함수를 제공하며, 이를 활용하여 무작위 데이터를 생성하거나 시뮬레이션을 수행할 수 있습니다. random.randrange() 함수를 사용하면 특정 범위 내에서 정수를 생성할 때 유용하게 사용됩니다.\n",
+    "# 주어진 리스트에서 무작위 선택\n",
+    "print(random.choice(['apple', 'banana', 'cherry'])) # random 모듈의 choice() 함수를 사용하여 리스트 ['apple', 'banana', 'cherry']에서 임의\n",
+    "\n",
+    "list1 = [1, 2, 3, 4, 5]\n",
+    "# 무작위로 썩기\n",
+    "random.shuffle(list1)\n",
+    "print(list1) # random 모듈의 shuffle() 함수를 사용하여 리스트 [1, 2, 3, 4, 5]의 요소들을 무작위로 섞습니다. random.shuffle() 함수는 리스트의 요소들을 제자리에서 무작위로 섞으며, 반환값은 없습니다. 예를 들어, random.shuffle([1, 2, 3, 4, 5])를 호출하면 리스트가 [3, 1, 5, 2, 4]와 같이 섞일 수 있습니다. random 모듈은 다양한 난수 생성 함수를 제공하며, 이를 활용하여 무작위 데이터를 생성하거나 시뮬레이션을 수행할 수 있습니다. random.shuffle() 함수를 사용하면 리스트의 요소들을 무작위로 섞을 때 유용하게 사용됩니다.\n",
+    "\n",
+    "print(random.sample(list1, k=2)) # random 모듈의 sample() 함수를 사용하여 리스트 [1, 2, 3, 4, 5]에서 임의로 2개의 요소를 선택합니다. random.sample() 함수는 주어진 리스트에서 지정된 개수만큼의 요소를 무작위로 선택하여 새로운 리스트로 반환합니다. 예를 들어, random.sample([1, 2, 3, 4, 5], 2)은 [2, 4]와 같은 값을 반환할 수 있습니다. random 모듈은 다양한 난수 생성 함수를 제공하며, 이를 활용하여 무작위 데이터를 생성하거나 시뮬레이션을 수행할 수 있습니다. random.sample() 함수를 사용하면 리스트에서 특정 개수의 요소를 무작위로 선택할 때 유용하게 사용됩니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48d9c2c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "지금부터 5초 정지\n",
+      "5초가 지났습니다.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "print(\"지금부터 5초 정지\")\n",
+    "time.sleep(5)\n",
+    "print(\"5초가 지났습니다.\")    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "ff8b943d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2026-05-13 15:10:28.636020\n"
+     ]
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "now = datetime.datetime.now() # datetime 모듈의 datetime 클래스에서 now() 메서드를 사용하여 현재 날짜와 시간을 가져옵니다. now() 메서드는 현재 날짜와 시간을 나타내는 datetime 객체를 반환합니다. 예를 들어, datetime.datetime.now()는 2024-06-01 12:34:56과 같은 값을 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. now() 메서드를 사용하면 현재 날짜와 시간을 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n",
+    "print(now) # 현재 날짜와 시간을 출력합니다. now 변수에는 datetime.datetime.now()를"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "277504ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2026\n",
+      "5\n",
+      "13\n",
+      "15\n",
+      "10\n",
+      "28\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(now.year) # 현재 연도를 출력합니다. now 변수는 datetime.datetime.now()로 생성된 datetime 객체를 참조하고 있으며, year 속성을 사용하여 현재 연도를 가져올 수 있습니다. 예를 들어, now.year은 2024를 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. year 속성을 사용하면 현재 연도를 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n",
+    "print(now.month) # 현재 월을 출력합니다. now 변수는 datetime.datetime.now()로 생성된 datetime 객체를 참조하고 있으며, month 속성을 사용하여 현재 월을 가져올\n",
+    "print(now.day) # 현재 일을 출력합니다. now 변수는 datetime.datetime.now()로 생성된 datetime 객체를 참조하고 있으며, day 속성을 사용하여 현재 일을 가져올 수 있습니다. 예를 들어, now.day은 1을 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. day 속성을 사용하면 현재 일을 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n",
+    "print(now.hour) # 현재 시를 출력합니다. now 변수는 datetime.datetime.now()로생성된 datetime 객체를 참조하고 있으며, hour 속성을 사용하여 현재 시를 가져올 수 있습니다. 예를 들어, now.hour은 12를 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. hour 속성을 사용하면 현재 시를 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n",
+    "print(now.minute) # 현재 분을 출력합니다. now 변수는 datetime.datetime.now()로생성된 datetime 객체를 참조하고 있으며, minute 속성을 사용하여 현재 분을 가져올 수 있습니다. 예를 들어, now.minute은 34를 반환할 수 있습니다. datetime 모듈은 날짜와 시간 관련 작업을 수행할 때 유용하게 사용됩니다. minute 속성을 사용하면 현재 분을 쉽게 가져올 수 있어, 다양한 응용 분야에서 활용할 수 있습니다.\n",
+    "print(now.second) # 현재 초를 출력합니다. now 변수는 datetime.datetime.now()로 생성된 datetime 객체를 참조하고 있으며, second 속성을 사용하여 현재 초를 가져올\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ee959ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n",
+      "Life is too short\n",
+      "You need python\n",
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('hello')\n",
+    "\n",
+    "# 줄단위 실행 >= 특정 행에서 실행 오류가 나는 경우\n",
+    "# 파이썬 자료형\n",
+    "# 정ㅅ형, 문자형, 불린형, 리스트, 튜플, 딕셔너리\n",
+    "\n",
+    "# \\n == 줄바꿈\n",
+    "# \\t == 탭\n",
+    "\n",
+    "multiline = \"Life is too short\\nYou need python\"\n",
+    "print(multiline)\n",
+    "\n",
+    "# import mod1\n",
+    "# print(mod1.add(3, 4))\n",
+    "# print(mod1.sub(10, 5))\n",
+    "\n",
+    "from mod1 import add\n",
+    "print(add(3, 7))\n",
+    "\n",
+    "from mod import *\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4044187",
+   "metadata": {},
+   "source": [
+    "### 예외처리\n",
+    "- 파이썬에서 오류를 처리하는 방법\n",
+    "\n",
+    "```\n",
+    "\n",
+    "try:\n",
+    "    오류 발생할 가능성 구문\n",
+    "except:\n",
+    "    오류 발생했을 때 처리방법\n",
+    "\n",
+    "try:\n",
+    "    오류 발생할 가능성 구문\n",
+    "except IndexError as 오류변수:\n",
+    "    IndexError 오류 발생했을 때 처리방법\n",
+    "\n",
+    "try:\n",
+    "\n",
+    "except IndexError as 오류변수:\n",
+    "    IndexError 오류 발생했을 때 처리방법\n",
+    "except ZeroDivisionError:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "04f4ca32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "오류발생\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    f = open(\"test3.txt\", \"r\")  \n",
+    "except:\n",
+    "    print(\"오류발생\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00aee51f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0으로 나눌 수 없습니다.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    4 / 0\n",
+    "except ZeroDivisionError:\n",
+    "    print(\"0으로 나눌 수 없습니다.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "id": "1e7e5828",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "division by zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    4 / 0\n",
+    "except ZeroDivisionError as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "621f45cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 나이를 입력받은 후 정수가 입력되었다면 age <= 18 미성년자입니다/ 환영합니다.\n",
+    "# 입력을 확인해 주세요\n",
+    "\n",
+    "try:\n",
+    "    age = int(input(\"나이 입력\"))    \n",
+    "except:\n",
+    "    print(\"입력을 확인해주세요.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "99605d96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 나만의 오류 작성하기\n",
+    "\n",
+    "class MyError(Exception):\n",
+    "    pass\n",
+    "\n",
+    "def say_nick(nick):\n",
+    "    if nick ==\"바보\":\n",
+    "        raise MyError()\n",
+    "    print(nick)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "79f198be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "천사\n"
+     ]
+    },
+    {
+     "ename": "MyError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mMyError\u001b[39m                                   Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[214]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m say_nick(\u001b[33m\"천사\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m say_nick(\u001b[33m\"바보\"\u001b[39m)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[211]\u001b[39m\u001b[32m, line 8\u001b[39m, in \u001b[36msay_nick\u001b[39m\u001b[34m(nick)\u001b[39m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m say_nick(nick):\n\u001b[32m      7\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m nick ==\u001b[33m\"바보\"\u001b[39m:\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m MyError()\n\u001b[32m      9\u001b[39m     print(nick)\n",
+      "\u001b[31mMyError\u001b[39m: "
+     ]
+    }
+   ],
+   "source": [
+    "say_nick(\"천사\")\n",
+    "say_nick(\"바보\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "bec3f8d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "JSONDecodeError",
+     "evalue": "Expecting value: line 1 column 1 (char 0)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mJSONDecodeError\u001b[39m                           Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[215]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m json\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m open(\u001b[33m\"test.json\"\u001b[39m, \u001b[33m\"r\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m     data = json.load(f)\n\u001b[32m      4\u001b[39m     print(type(data))\n\u001b[32m      5\u001b[39m     print(data)\n\u001b[32m      6\u001b[39m \n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.4_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/__init__.py:298\u001b[39m, in \u001b[36mload\u001b[39m\u001b[34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[39m\n\u001b[32m    278\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mload\u001b[39m(fp, *, \u001b[38;5;28mcls\u001b[39m=\u001b[38;5;28;01mNone\u001b[39;00m, object_hook=\u001b[38;5;28;01mNone\u001b[39;00m, parse_float=\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m    279\u001b[39m         parse_int=\u001b[38;5;28;01mNone\u001b[39;00m, parse_constant=\u001b[38;5;28;01mNone\u001b[39;00m, object_pairs_hook=\u001b[38;5;28;01mNone\u001b[39;00m, **kw):\n\u001b[32m    280\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Deserialize ``fp`` (a ``.read()``-supporting file-like object containing\u001b[39;00m\n\u001b[32m    281\u001b[39m \u001b[33;03m    a JSON document) to a Python object.\u001b[39;00m\n\u001b[32m    282\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    296\u001b[39m \u001b[33;03m    kwarg; otherwise ``JSONDecoder`` is used.\u001b[39;00m\n\u001b[32m    297\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m298\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mloads\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mfp\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mread\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    299\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mcls\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcls\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mobject_hook\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mobject_hook\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    300\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mparse_float\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mparse_float\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mparse_int\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mparse_int\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    301\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mparse_constant\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mparse_constant\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mobject_pairs_hook\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mobject_pairs_hook\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkw\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.4_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/__init__.py:352\u001b[39m, in \u001b[36mloads\u001b[39m\u001b[34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[39m\n\u001b[32m    347\u001b[39m     s = s.decode(detect_encoding(s), \u001b[33m'\u001b[39m\u001b[33msurrogatepass\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m    349\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[32m    350\u001b[39m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[32m    351\u001b[39m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[32m--> \u001b[39m\u001b[32m352\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m_default_decoder\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdecode\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43ms\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    353\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    354\u001b[39m     \u001b[38;5;28mcls\u001b[39m = JSONDecoder\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.4_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/decoder.py:345\u001b[39m, in \u001b[36mJSONDecoder.decode\u001b[39m\u001b[34m(self, s, _w)\u001b[39m\n\u001b[32m    340\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, s, _w=WHITESPACE.match):\n\u001b[32m    341\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[32m    342\u001b[39m \u001b[33;03m    containing a JSON document).\u001b[39;00m\n\u001b[32m    343\u001b[39m \n\u001b[32m    344\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m345\u001b[39m     obj, end = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mraw_decode\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43ms\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43midx\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m_w\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43ms\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mend\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    346\u001b[39m     end = _w(s, end).end()\n\u001b[32m    347\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m end != \u001b[38;5;28mlen\u001b[39m(s):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.4_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/json/decoder.py:363\u001b[39m, in \u001b[36mJSONDecoder.raw_decode\u001b[39m\u001b[34m(self, s, idx)\u001b[39m\n\u001b[32m    361\u001b[39m     obj, end = \u001b[38;5;28mself\u001b[39m.scan_once(s, idx)\n\u001b[32m    362\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m--> \u001b[39m\u001b[32m363\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[33m\"\u001b[39m\u001b[33mExpecting value\u001b[39m\u001b[33m\"\u001b[39m, s, err.value) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    364\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n",
+      "\u001b[31mJSONDecodeError\u001b[39m: Expecting value: line 1 column 1 (char 0)"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "with open(\"test.json\", \"r\") as f:\n",
+    "    data = json.load(f)\n",
+    "    print(type(data))\n",
+    "    print(data)\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0b9ada0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'3.53.0'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# SQLite 내장 db 사용 => sql\n",
+    "\n",
+    "import sqlite3\n",
+    "\n",
+    "sqlite3.sqlite_version\n",
+    "sqlite3.sqlite_version_info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "87d44d39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sqlite3.Cursor at 0x10d1e55c0>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sqlite3\n",
+    "\n",
+    "conn = sqlite3.connect(\"test.db\", isolation_level=None)\n",
+    "\n",
+    "# cursor : 특정 테이블에 접근하는 포인터\n",
+    "cursor = conn.cursor()\n",
+    "\n",
+    "sql = \"CREATE TABLE IF NOT EXISTS users(id interger primary key, username text,\" \\\n",
+    "    \"email text, phone text, website text, regdate text)\"\n",
+    "    \n",
+    "cursor.execute(sql)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dadbbb81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'26-05-14 13:02:23'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "now = datetime.now()\n",
+    "\n",
+    "# now의 format 변경\n",
+    "nowDateTime = now.strftime('%y-%m-%d %H:%M:%S')\n",
+    "nowDateTime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "53ad6ae9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IntegrityError",
+     "evalue": "UNIQUE constraint failed: users.id",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mIntegrityError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[51]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# 데이터 삽입\u001b[39;00m\n\u001b[32m      2\u001b[39m sql = f\"INSERT INTO users(id, username, email, phone, website, regdate)\" \\\n\u001b[32m      3\u001b[39m \u001b[33m\"values(1, 'kk', 'kk@kk.kk', '010-0000-0000', 'https://www.kk.com', '{nowDateTime}')\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m cursor.execute(sql)\n",
+      "\u001b[31mIntegrityError\u001b[39m: UNIQUE constraint failed: users.id"
+     ]
+    }
+   ],
+   "source": [
+    "# 데이터 삽입\n",
+    "sql = f\"INSERT INTO users(id, username, email, phone, website, regdate)\" \\\n",
+    "\"values(1, 'kk', 'kk@kk.kk', '010-0000-0000', 'https://www.kk.com', '{nowDateTime}')\"\n",
+    "cursor.execute(sql)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "5d92f698",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IntegrityError",
+     "evalue": "UNIQUE constraint failed: users.id",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mIntegrityError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[52]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# 데이터 삽입\u001b[39;00m\n\u001b[32m      2\u001b[39m sql = f\"INSERT INTO users(id, username, email, phone, website, regdate)\" \\\n\u001b[32m      3\u001b[39m \u001b[33m\"values(?,?,?,?,?,?)\"\u001b[39m\n\u001b[32m      4\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m cursor.execute(sql, (\u001b[32m2\u001b[39m, \u001b[33m'kk'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, \u001b[33m'010-0000-0000'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, nowDateTime))\n",
+      "\u001b[31mIntegrityError\u001b[39m: UNIQUE constraint failed: users.id"
+     ]
+    }
+   ],
+   "source": [
+    "# 데이터 삽입\n",
+    "sql = f\"INSERT INTO users(id, username, email, phone, website, regdate)\" \\\n",
+    "\"values(?,?,?,?,?,?)\"\n",
+    "\n",
+    "cursor.execute(sql, (2, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', nowDateTime))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "1277e5d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IntegrityError",
+     "evalue": "UNIQUE constraint failed: users.id",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mIntegrityError\u001b[39m                            Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[53]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m      3\u001b[39m     (\u001b[32m4\u001b[39m, \u001b[33m'kk'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, \u001b[33m'010-0000-0000'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, nowDateTime),\n\u001b[32m      4\u001b[39m     (\u001b[32m5\u001b[39m, \u001b[33m'kk'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, \u001b[33m'010-0000-0000'\u001b[39m, \u001b[33m'kk@kk.com'\u001b[39m, nowDateTime)\n\u001b[32m      5\u001b[39m )\n\u001b[32m      6\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m cursor.executemany(sql, userList)\n",
+      "\u001b[31mIntegrityError\u001b[39m: UNIQUE constraint failed: users.id"
+     ]
+    }
+   ],
+   "source": [
+    "userList = (\n",
+    "    (3, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', nowDateTime),\n",
+    "    (4, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', nowDateTime),\n",
+    "    (5, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', nowDateTime)\n",
+    ")\n",
+    "\n",
+    "cursor.executemany(sql, userList)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "636489de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 'kk', 'kk@kk.kk', '010-0000-0000', 'https://www.kk.com', '{nowDateTime}')\n",
+      "(2, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', '26-05-13 17:47:38')\n",
+      "(3, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', '26-05-13 17:47:38')\n",
+      "(4, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', '26-05-13 17:47:38')\n",
+      "(5, 'kk', 'kk@kk.com', '010-0000-0000', 'kk@kk.com', '26-05-13 17:47:38')\n"
+     ]
+    }
+   ],
+   "source": [
+    "sql = cursor.execute(\"SELECT * FROM users\")\n",
+    "\n",
+    "for c in cursor.fetchall():\n",
+    "    print(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b604b50e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# [13]번 셀 끝에 추가하거나 [14]번 셀 시작 시 실행\n",
+    "# 만약 이전 연결에서 트랜잭션이 남아있다면 강제로 종료합니다.\n",
+    "conn.rollback() # 혹은 conn.commit()\n",
+    "\n",
+    "# [14]번 셀 수정\n",
+    "sql = \"UPDATE users SET regdate=? WHERE id=1\"\n",
+    "cursor.execute(sql, (nowDateTime,))\n",
+    "conn.commit() # 변경 사항을 확실히 반영하고 락을 해제합니다.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "714326dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sqlite3.Cursor at 0x10d1e55c0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql =\"DELETE FROM users WHERE id=3\"\n",
+    "cursor.execute(sql)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "6caa3191",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sqlite3.Cursor at 0x10d1e55c0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sql=\"DELETE FROM users\"\n",
+    "cursor.execute(sql)"
+   ]
   }
  ],
  "metadata": {
@@ -1505,7 +5570,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.14.4"
+   "version": "3.14.5"
   }
  },
  "nbformat": 4,
diff --git a/pythonSource/Basic/hello.py b/pythonSource/Basic/hello.py
index be2f9af..069c7f3 100644
--- a/pythonSource/Basic/hello.py
+++ b/pythonSource/Basic/hello.py
@@ -22,5 +22,18 @@ print(f)
 print(g)
 print(h)
 
+# \n == enter
 multiline = "Life is too short\n you need python"
-print(multiline)
\ No newline at end of file
+print(multiline)
+
+# import mod1
+# print(mod1.add(5,3))
+# print(mod1.sub(5,3))
+
+# from mod1 import add
+# print(add(3,7))
+
+# * : 모두
+from mod1 import *
+print(add(3,7))
+print(sub(3,7))
\ No newline at end of file
diff --git a/pythonSource/Basic/info.txt b/pythonSource/Basic/info.txt
new file mode 100644
index 0000000..d62a44c
--- /dev/null
+++ b/pythonSource/Basic/info.txt
@@ -0,0 +1,500 @@
+파다 98 170
+차카 63 194
+바카 42 152
+마파 62 157
+라마 77 156
+카다 40 143
+가하 63 150
+라가 78 153
+라자 88 189
+파다 47 186
+파가 50 168
+바타 59 194
+파사 63 196
+하차 87 195
+마파 65 152
+하파 62 145
+차바 69 159
+카차 70 196
+바라 48 147
+자나 43 146
+파마 88 149
+나나 67 155
+파가 49 152
+자차 96 140
+사마 88 184
+사바 90 174
+나라 58 167
+파나 66 152
+타나 86 170
+아나 93 195
+사하 61 151
+타하 85 142
+카차 73 140
+카마 90 157
+가가 41 180
+나나 56 198
+다파 40 161
+나카 62 170
+차다 88 187
+가자 45 150
+사다 80 195
+카바 84 192
+하자 98 164
+카자 92 177
+다다 41 150
+아타 86 170
+마카 50 182
+마자 72 175
+사다 40 158
+사아 93 174
+가라 58 153
+자아 77 196
+아바 42 162
+마하 56 175
+파파 93 187
+파마 91 172
+카다 80 146
+가다 95 166
+카카 42 150
+라차 50 191
+아바 95 143
+바마 91 175
+파라 85 164
+마라 85 168
+자하 89 192
+사아 99 197
+사차 61 172
+차차 76 162
+다차 83 191
+나타 63 150
+하사 91 150
+카사 59 162
+카차 87 197
+카바 61 181
+바파 58 168
+카타 93 200
+나나 51 194
+다가 92 169
+가나 82 145
+카파 77 177
+타하 54 157
+차바 44 165
+사파 58 142
+나카 83 157
+다가 59 151
+마카 45 144
+마파 72 169
+라아 54 188
+타자 89 177
+나나 48 198
+파사 75 168
+바다 95 157
+아아 58 189
+아가 72 174
+카아 78 156
+타바 50 168
+다사 87 163
+라다 83 145
+다라 97 140
+마가 88 143
+라파 88 183
+하카 51 199
+자다 41 172
+자하 70 175
+마다 66 184
+바자 93 173
+가차 100 175
+마아 62 151
+파타 45 178
+타라 80 165
+마하 66 187
+라카 50 182
+카다 47 181
+가가 51 144
+파아 65 189
+타자 67 160
+카아 51 197
+가라 79 151
+자아 58 192
+하하 44 152
+카다 96 170
+마라 84 176
+마아 67 179
+차다 67 147
+아라 71 169
+타라 50 154
+바다 52 186
+하차 40 145
+파사 75 161
+가다 66 147
+라하 84 177
+라바 53 163
+다사 61 155
+아바 57 192
+마다 55 172
+타카 81 159
+타라 74 191
+다차 62 142
+가하 48 171
+차바 94 166
+나파 79 180
+차사 80 140
+하다 51 196
+바나 54 148
+마카 66 163
+하나 78 169
+마나 85 171
+자마 74 152
+파나 41 170
+라나 88 149
+다가 73 158
+자아 78 163
+아나 47 167
+바마 54 144
+사바 47 177
+자타 58 156
+차나 52 177
+타카 69 174
+사자 54 178
+바나 49 143
+라하 87 177
+마차 73 199
+아자 61 152
+마파 41 177
+카나 76 145
+마타 57 194
+차파 90 169
+가아 58 194
+나다 85 171
+다라 89 171
+다하 87 152
+차나 71 147
+바마 71 143
+자가 64 176
+파나 75 183
+라파 99 185
+라사 91 173
+바다 59 191
+자하 56 179
+아나 79 163
+마아 67 183
+자파 99 198
+마사 89 154
+사다 90 190
+타마 76 152
+바가 82 176
+마타 47 180
+마하 93 200
+아파 100 183
+카가 81 178
+바하 96 145
+하마 61 185
+바마 58 151
+카사 100 179
+마카 67 159
+마나 74 195
+하다 76 162
+타자 69 177
+바라 59 149
+자파 58 181
+바나 79 154
+차아 91 163
+카라 63 179
+가카 60 197
+가바 54 185
+바나 58 157
+차바 57 183
+가사 66 163
+바차 74 142
+다파 56 183
+아가 70 141
+가차 46 141
+라아 76 140
+하타 70 158
+다사 90 177
+아자 54 152
+라라 40 198
+하다 77 164
+가카 89 183
+자하 94 145
+가타 85 177
+나아 92 162
+다카 77 171
+다마 88 156
+바라 100 193
+하카 55 194
+자파 42 150
+아자 82 155
+마마 46 144
+카가 96 147
+마자 50 148
+아라 47 160
+카라 99 176
+타바 56 186
+타아 64 173
+바파 86 192
+마사 78 176
+아파 62 193
+카바 91 164
+마타 62 161
+마가 92 199
+라바 50 177
+라라 68 158
+차파 40 176
+타나 58 147
+라하 83 158
+자바 79 167
+하아 53 140
+다라 93 185
+다나 78 184
+아다 53 176
+타다 55 146
+하자 67 198
+사바 47 149
+타나 83 185
+타자 43 194
+파파 52 200
+차타 84 144
+가가 92 196
+라하 47 144
+차사 60 174
+마타 65 179
+다사 63 148
+카아 79 142
+라카 73 161
+카아 92 141
+라라 92 151
+차카 71 183
+하타 91 146
+사사 82 194
+바파 58 180
+카가 40 176
+차다 72 182
+파가 95 163
+파아 82 196
+타가 40 143
+나나 69 176
+카카 98 171
+차라 77 198
+파바 54 140
+다다 96 198
+바파 77 141
+자라 95 171
+파마 90 178
+다바 100 168
+타타 94 187
+타파 90 147
+차사 43 142
+자마 64 194
+가파 89 192
+파파 93 189
+라가 48 182
+마타 49 166
+다타 84 157
+타가 76 160
+하자 46 140
+자다 45 182
+카타 49 162
+카아 85 170
+바자 56 157
+아라 73 180
+라아 98 200
+아차 55 143
+파라 58 149
+파바 44 186
+하자 83 197
+아차 67 155
+카하 51 169
+하바 56 144
+타카 91 155
+차마 86 158
+차아 96 154
+다사 43 144
+파다 70 200
+나아 60 153
+하아 69 194
+하가 94 185
+마가 51 168
+가파 48 150
+바바 68 191
+나아 48 171
+파아 45 140
+바하 53 143
+바파 68 154
+차마 88 193
+다타 88 167
+차나 67 200
+라바 76 185
+바다 44 175
+가마 64 166
+바차 60 159
+카라 88 150
+자가 49 146
+자가 75 165
+가아 75 144
+자라 75 143
+마자 41 167
+가차 81 160
+다바 62 167
+카바 71 168
+타타 55 197
+카자 44 200
+아아 52 188
+사사 59 155
+가라 71 163
+바사 44 189
+가차 84 145
+나자 67 190
+차마 89 148
+하마 89 183
+다사 71 176
+차가 97 200
+다하 87 198
+아가 56 170
+사자 55 160
+카자 77 169
+파하 53 168
+아사 95 172
+차타 81 178
+사차 42 145
+나라 52 143
+자아 45 145
+다사 85 186
+아자 77 151
+타카 90 190
+아나 59 152
+라타 40 149
+하타 64 168
+하나 92 171
+자타 56 198
+사타 80 192
+라다 68 193
+타다 51 154
+차가 57 193
+라카 70 171
+파나 87 143
+하마 92 153
+마바 52 198
+카아 73 185
+아라 87 176
+다파 71 182
+라자 55 166
+마가 84 153
+자사 79 195
+나타 63 160
+라사 52 194
+타마 62 185
+하자 93 152
+바차 50 161
+사자 99 141
+바가 72 182
+바나 88 181
+차파 95 143
+차다 69 145
+라가 92 178
+타파 97 180
+마파 99 183
+바타 97 146
+라타 74 143
+바아 40 162
+사타 66 158
+하자 92 182
+차바 93 171
+아하 42 177
+다타 71 190
+바가 72 186
+마다 52 146
+자다 88 181
+라다 48 148
+타마 40 152
+파타 67 151
+바차 70 158
+차자 92 172
+라마 96 153
+하나 54 141
+라라 60 167
+하다 54 164
+라파 79 155
+파다 47 184
+사하 92 186
+다나 99 173
+사타 43 188
+타하 88 174
+바바 70 175
+아가 80 155
+사카 72 186
+라타 51 141
+차마 74 144
+라자 98 197
+차가 71 167
+가파 73 187
+마마 79 146
+아아 93 142
+마하 53 164
+가아 42 169
+가차 62 142
+바가 80 158
+바자 99 149
+라바 51 159
+라아 69 143
+타바 80 140
+자하 76 186
+바나 62 171
+바아 82 167
+하하 96 143
+아파 53 145
+타차 93 156
+가다 41 171
+바사 43 159
+자라 57 181
+다다 67 172
+라카 67 162
+다마 71 156
+사카 48 186
+가가 58 153
+라라 54 166
+아타 59 171
+바라 61 174
+자마 75 195
+사타 82 166
+라라 74 164
+하바 47 183
+나마 46 179
+라사 40 190
+마나 77 181
+가가 52 145
+자차 42 156
+차나 76 157
+자다 73 192
+라다 93 194
+나자 98 199
+아카 84 194
+나파 52 144
+타차 82 191
+가나 75 196
+라다 48 170
+자자 93 193
+타다 48 196
+차가 83 197
+자하 76 165
+하자 61 142
+하타 74 199
+타다 83 141
+하카 58 150
+자차 72 157
+하가 47 156
+마나 64 141
+아파 65 162
+다아 94 185
+다아 68 164
+타파 62 147
+카바 57 183
+하하 84 151
+바하 92 140
+자마 80 183
+카나 59 193
+사자 86 146
+나차 88 168
+다마 43 161
+사타 86 149
diff --git a/pythonSource/Basic/mod1.py b/pythonSource/Basic/mod1.py
new file mode 100644
index 0000000..1b0b8b5
--- /dev/null
+++ b/pythonSource/Basic/mod1.py
@@ -0,0 +1,12 @@
+# 모듈 생성
+def add(a, b):
+    return a + b
+
+def sub(a, b):
+    return a - b
+
+# 모듈 안 기능들이 잘 만들어졌는지 확인
+# mod1 직접 실행될 때만 if문 실행
+if __name__ == "__main__" : 
+    print(add(3, 4))
+    print(sub(6, 4))
\ No newline at end of file
diff --git a/pythonSource/Basic/test.db b/pythonSource/Basic/test.db
new file mode 100644
index 0000000..ab9ecaf
Binary files /dev/null and b/pythonSource/Basic/test.db differ
diff --git a/pythonSource/Basic/test.json b/pythonSource/Basic/test.json
new file mode 100644
index 0000000..8166253
--- /dev/null
+++ b/pythonSource/Basic/test.json
@@ -0,0 +1,10 @@
+{
+    "name": "John Doe",
+    "age": 30,
+    "isStudent": false,
+    "hobbies": ["reading", "coding"]
+    "address": {
+        "city": "Seoul",
+        "zipcode": "12345"  
+    }
+}
\ No newline at end of file
diff --git a/pythonSource/Basic/test.txt b/pythonSource/Basic/test.txt
new file mode 100644
index 0000000..30d74d2
--- /dev/null
+++ b/pythonSource/Basic/test.txt
@@ -0,0 +1 @@
+test
\ No newline at end of file
diff --git a/pythonSource/Basic/test1.json b/pythonSource/Basic/test1.json
new file mode 100644
index 0000000..4c20f26
--- /dev/null
+++ b/pythonSource/Basic/test1.json
@@ -0,0 +1 @@
+{"name": "\ud64d\uae38\ub3d9", "birth": "0525", "age": 30}
\ No newline at end of file
diff --git a/pythonSource/Basic/test2.txt b/pythonSource/Basic/test2.txt
new file mode 100644
index 0000000..6ad36e5
--- /dev/null
+++ b/pythonSource/Basic/test2.txt
@@ -0,0 +1,3 @@
+Line 1
+Line 2
+Line 3
diff --git a/pythonSource/gradio/app1.py b/pythonSource/gradio/app1.py
new file mode 100644
index 0000000..a8341f8
--- /dev/null
+++ b/pythonSource/gradio/app1.py
@@ -0,0 +1,13 @@
+import gradio as gr
+
+def greet(name, intensity):
+    return "Hello, " + name + "!" * int(intensity)
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=["text", "slider"],
+    outputs=["text"],
+    api_name="predict"
+)
+
+demo.launch()
\ No newline at end of file