Source/colab/트랜스포머로 무엇을 할 수 있나요.ipynb

{"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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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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