Mingu Kim
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181 KiB
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Executable File
{"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 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\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 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*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/transformers/utils/generic.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, 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\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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