diff --git "a/natural_language_inference.ipynb" "b/natural_language_inference.ipynb" new file mode 100644--- /dev/null +++ "b/natural_language_inference.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["# 이거 kor nli로 바꿔야댐"],"metadata":{"id":"DnWp_5WrB28c"},"id":"DnWp_5WrB28c"},{"cell_type":"markdown","id":"_kbUSCb_Egcu","metadata":{"id":"_kbUSCb_Egcu"},"source":["# HuggingFace Transformers를 활용한 문장 분류 모델 학습"]},{"cell_type":"markdown","id":"of2izij8Eqe0","metadata":{"id":"of2izij8Eqe0"},"source":["본 노트북에서는 `klue/roberta-base` 모델을 **KLUE** 내 **NLI** 데이터셋을 활용하여 모델을 훈련하는 예제를 다루게 됩니다.\n","\n","학습을 통해 얻어질 `klue-roberta-base-nli` 모델은 입력된 두 문장의 추론 관계를 예측하는데 사용할 수 있게 됩니다.\n","\n","학습 과정 이후에는 간단한 예제 코드를 통해 모델이 어떻게 활용되는지도 함께 알아보도록 할 것입니다.\n","\n","모든 소스 코드는 [`huggingface-notebooks`](https://github.com/huggingface/notebooks)를 참고하였습니다.\n","\n","먼저, 노트북을 실행하는데 필요한 라이브러리를 설치합니다. 모델 훈련을 위해서는 `transformers`가, 학습 데이터셋 로드를 위해서는 `datasets` 라이브러리의 설치가 필요합니다. 그 외 모델 성능 검증을 위해 `scipy`, `scikit-learn`을 추가로 설치해주도록 합니다."]},{"cell_type":"code","execution_count":1,"id":"8e59dc52","metadata":{"id":"8e59dc52","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687392473960,"user_tz":-540,"elapsed":20526,"user":{"displayName":"김평진","userId":"11705489940506901507"}},"outputId":"fda59d04-ce30-48e2-b15f-2d271ca3db0f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n"," Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m106.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting datasets\n"," Downloading datasets-2.13.0-py3-none-any.whl (485 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m485.6/485.6 kB\u001b[0m \u001b[31m52.1 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aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.13.0 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.15.1 multidict-6.0.4 multiprocess-0.70.14 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.2 xxhash-3.2.0 yarl-1.9.2\n"]}],"source":[" !pip install -U transformers datasets scipy accelerate # 재시작 하기"]},{"cell_type":"markdown","id":"8wSLfX-jaIqV","metadata":{"id":"8wSLfX-jaIqV"},"source":["## 문장 분류 모델 학습"]},{"cell_type":"markdown","id":"92e868b7-3cf7-4977-a6e7-ebbb99ba298e","metadata":{"id":"92e868b7-3cf7-4977-a6e7-ebbb99ba298e"},"source":["노트북을 실행하는데 필요한 라이브러리들을 모두 임포트합니다."]},{"cell_type":"code","execution_count":1,"id":"243b461f","metadata":{"id":"243b461f","executionInfo":{"status":"ok","timestamp":1687392696445,"user_tz":-540,"elapsed":5491,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[],"source":["import random\n","import logging\n","from IPython.display import display, HTML\n","\n","import numpy as np\n","import pandas as pd\n","import datasets\n","from datasets import load_dataset, load_metric, ClassLabel, Sequence, concatenate_datasets\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer"]},{"cell_type":"markdown","id":"59b1e5ae-471f-461f-850f-4241a6037471","metadata":{"id":"59b1e5ae-471f-461f-850f-4241a6037471"},"source":["학습에 필요한 정보를 변수로 기록합니다.\n","\n","본 노트북에서는 `klue-roberta-base` 모델을 활용하지만, https://huggingface.co/klue 페이지에서 더 다양한 사전학습 언어 모델을 확인하실 수 있습니다.\n","\n","학습 태스크로는 `nli`를, 배치 사이즈로는 32를 지정하겠습니다."]},{"cell_type":"code","execution_count":2,"id":"6ecaacbe","metadata":{"id":"6ecaacbe","executionInfo":{"status":"ok","timestamp":1687392696446,"user_tz":-540,"elapsed":4,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[],"source":["model_checkpoint = \"klue/roberta-base\"\n","batch_size = 32"]},{"cell_type":"markdown","id":"816b5893-d03e-4c6e-8536-e246b1def987","metadata":{"id":"816b5893-d03e-4c6e-8536-e246b1def987"},"source":["이제 HuggingFace `datasets` 라이브러리에 등록된 KLUE 데이터셋 중, NLI 데이터를 내려받습니다."]},{"cell_type":"code","source":["datasets1 = load_dataset(\"kor_nli\",'multi_nli')\n","datasets2 = load_dataset(\"kor_nli\",'snli')\n","datasets3 = load_dataset(\"kor_nli\",'xnli')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":188,"referenced_widgets":["466f255a18e44009b0ae885bf446fd53","d50d6c6cad264ba1bed5bf10ac3a16af","e0495edd56ce4cbdb0bd5734db875658","d90a70724ee4484ca95f4cd31e4f7ed6","f285f14c66824bd9b11ce0bf197ec429","610bef5c566c48a5ac59006486fa6c70","53c5632b1d4041c98c7d810c57e57397","0342e3f4a098470b8fbd0ed9856c411d","78e94cd7906744d6aefd03f22286107c","9bff1ea26cd144ffb64b9982cc0797a5","0ebc1e2ce2cc408d997a603a863e0234","f4ecbbcdfed74b03b6cd744858ef79a6","dbd3b9a00f504f40b5caad6d94620a6a","747f15a8b4ae45238bc14a2c050020f0","7effa5c9e4864e9a8476de204fd343cd","f455226fa15044ae89f61b5a2d5d459d","5a1036e4be2b4a14851e242f52ce505d","bb7995eda272473fb0fc3c4321be38ba","6782d749764d4f36bad3ad75d5d0e213","125b2a24665242309babb4e529c19c1f","c280153ea7464a9493de90fce672cbcf","42b937af8f1e4e62b8928acdc429bfd8","07a7715f1d274ee7bf941c9931d1e05f","a003451748ab4b95b8309bc6d8904d07","c8e69b089ff64d0a9fc0f5d79badfa14","8a89a9faa4db4bb88a1f5f36a0b6580c","a1e33c3128134a76a5ecfe07e468d2fa","02e0266a0b7b4f5b80a495dd8a4e71ad","5c379b3f55444b4abef930a3f71c686e","ef44168afa25491599adc6aed2960ad7","a7c66e93824a4768a0540b39ca118b57","160202d9302c4f0aa36c78b870e49fb6","0a92564fb1484de6a54b8375a71c3661"]},"id":"18DoQr8KD-1J","executionInfo":{"status":"ok","timestamp":1687392702905,"user_tz":-540,"elapsed":6462,"user":{"displayName":"김평진","userId":"11705489940506901507"}},"outputId":"e1a2feb3-c459-41e9-8529-02b57318995b"},"id":"18DoQr8KD-1J","execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Found cached dataset kor_nli (/root/.cache/huggingface/datasets/kor_nli/multi_nli/1.0.0/06d9b61bd1372a618df02294965857ff10886d48696f33a32cbea656b71dfcf0)\n"]},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1 [00:00"],"text/html":["\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
premisehypothesislabel
0문학은 예이츠, 쇼, 베켓의 노벨상 수상자 3명을 배출한 유일한 도시인 더블린에서 항상 번창해 왔다.노벨 문학상 수상자인 예이츠는 더블린을 방문한 적이 없다.contradiction
1좋아요, 좋은 저녁 되세요나가는 길에 비가 왔으면 좋겠다.contradiction
2존 캐번디쉬는 플랫폼에서 기다리고 있었고, 나를 차로 안내했다.우리는 함께 비즈니스 미팅으로 향하려고 했다.neutral
3그리고 말레이시아는 일년 내내 행사와 축제의 축제를 선보인다.말레이시아는 많은 행사로 인해 인기 있는 관광지입니다.neutral
4사암의 색은 자이푸르에게 분홍색 도시라는 이름을 얻었지만, 로즈핑크에서 가장 따뜻한 호박, 밝은 오렌지, 그리고 둔한 오크레에 이르기까지 하루의 계절과 시간에 따라 색이 변한다는 것을 알게 될 것이다.사암의 색은 녹색이다.contradiction
539 - 연방 직원은 사회 보장 40 및 메디 케어와 같은 사회 보험 프로그램에 의해 적용 대상 인구의 나머지 부분과 동일한 조건으로 적용될 수 있습니다.작년에 10만 명의 연방 직원들이 사회 서비스를 이용했다.neutral
6그리고 굿윈과 같은 몇몇 사람들은 전체주의와의 전쟁이 경제 발전을 가능하게 했다고 주장하지만, 센과 같은 다른 사람들은 그렇다.굿윈은 전쟁과 경제 발전 사이의 연관성을 본다.entailment
7난 루이스빌에 있어나는 다음 주에 루이스빌에 있을 것이다.contradiction
8그리고 그들이 그 장면에 사용한 버팔로가 닐 영의 버팔로라는 것을 촬영하려고 할 때, 나는 그들이 그의 이름이 무엇이라고 말했는지 기억할 수 없지만 그는 오레오 쿠키에 대한 페티쉬를 가지고 있다.그는 오레오 쿠키 먹는 것을 좋아한다entailment
9어떤 종류의 풀을 가지고 있나요?손에 들고 있는 풀의 종류는 무엇인가?entailment
"]},"metadata":{}}],"source":["show_random_elements(datasets[\"train\"])"]},{"cell_type":"markdown","id":"zakMS4Y61sLm","metadata":{"id":"zakMS4Y61sLm"},"source":["훈련 과정 중 모델의 성능을 파악하기 위한 메트릭을 설정합니다.\n","\n","`datasets` 라이브러리에는 이미 구현된 메트릭을 사용할 수 있는 `load_metric` 함수가 있습니다.\n","\n","그 중 **GLUE** 데이터셋에 이미 다양한 메트릭이 구현되어 있으므로, **GLUE** 그 중에서도 **KLUE NLI**와 동일한 `accuracy` 메트릭을 사용하는 `qnli` 태스크의 메트릭을 사용합니다."]},{"cell_type":"code","execution_count":8,"id":"5c214cfb-efa2-4e31-9268-9d1038626095","metadata":{"id":"5c214cfb-efa2-4e31-9268-9d1038626095","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1687392774983,"user_tz":-540,"elapsed":536,"user":{"displayName":"김평진","userId":"11705489940506901507"}},"outputId":"07fed33a-fd48-408d-e0be-aaf050cd69af"},"outputs":[{"output_type":"stream","name":"stderr","text":[":1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n"," metric = load_metric(\"glue\", \"qnli\")\n"]}],"source":["metric = load_metric(\"glue\", \"qnli\")"]},{"cell_type":"markdown","id":"I07wSysL2fRs","metadata":{"id":"I07wSysL2fRs"},"source":["`accuracy` 메트릭이 정상적으로 작동하는지 확인하기 위해, 랜덤한 예측 값과 라벨 값을 생성합니다."]},{"cell_type":"code","execution_count":9,"id":"e988c49f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e988c49f","outputId":"da777fd3-8ee6-4cef-ad0a-5985021ff662","executionInfo":{"status":"ok","timestamp":1687392774984,"user_tz":-540,"elapsed":11,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n"," 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n"," 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0]),\n"," array([0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n"," 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0,\n"," 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0]))"]},"metadata":{},"execution_count":9}],"source":["fake_preds = np.random.randint(0, 2, size=(64,))\n","fake_labels = np.random.randint(0, 2, size=(64,))\n","fake_preds, fake_labels"]},{"cell_type":"markdown","id":"ryxIEDZR2oR-","metadata":{"id":"ryxIEDZR2oR-"},"source":["앞서 생성한 랜덤 예측, 랜덤 라벨 값을 `compute()` 함수에 입력해 잘 동작하는지 확인해봅시다."]},{"cell_type":"code","execution_count":10,"id":"e2dcf392-5c0a-4bbb-b9f6-cc42be6a382a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e2dcf392-5c0a-4bbb-b9f6-cc42be6a382a","outputId":"7aeb5077-3883-49fd-9891-6231ce41e870","executionInfo":{"status":"ok","timestamp":1687392774984,"user_tz":-540,"elapsed":10,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'accuracy': 0.53125}"]},"metadata":{},"execution_count":10}],"source":["metric.compute(predictions=fake_preds, references=fake_labels)"]},{"cell_type":"markdown","id":"rPvvy_wC24i_","metadata":{"id":"rPvvy_wC24i_"},"source":["이제 학습에 활용할 토크나이저를 로드해오도록 합니다."]},{"cell_type":"code","execution_count":11,"id":"b64e70af","metadata":{"id":"b64e70af","executionInfo":{"status":"ok","timestamp":1687392774984,"user_tz":-540,"elapsed":7,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[],"source":["tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)"]},{"cell_type":"markdown","id":"YIVjwvm428Q_","metadata":{"id":"YIVjwvm428Q_"},"source":["로드된 토크나이저가 두 개 문장을 토큰화하는 방식을 파악하기 위해 두 문장을 입력 값으로 넣어줘보도록 합시다."]},{"cell_type":"code","execution_count":12,"id":"0520aba3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0520aba3","outputId":"f7180410-b147-4baa-c336-80a2098610fa","executionInfo":{"status":"ok","timestamp":1687392774984,"user_tz":-540,"elapsed":7,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [0, 3997, 2116, 11451, 7777, 2138, 4543, 4402, 2200, 7590, 2088, 1513, 2062, 18, 2, 3997, 2259, 1120, 6233, 5756, 11314, 2], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"]},"metadata":{},"execution_count":12}],"source":["tokenizer(\"남자가 공구 상자를 인도 아래로 옮기고 있다.\", \"남자는 밖으로 걸어 나간다\")"]},{"cell_type":"markdown","id":"FDAaxujL7l1X","metadata":{"id":"FDAaxujL7l1X"},"source":["`input_ids`를 보시면 `cls_token`에 해당하는 2번 토큰이 가장 좌측에 붙게 되며, `sep_token`의 3번 토큰이 각각 중간과 가장 우측에 더해진 것을 확인할 수 있습니다.\n","\n","이제 앞서 로드한 데이터셋에서 각 문장에 해당하는 *value* 를 뽑아주기 위한 *key* 를 정의합니다.\n","\n","앞서 **KLUE NLI** 데이터셋의 두 문장은 각각 `premise`와 `hypothesis`라는 이름으로 정의된 것을 확인하였으니, 두 문장의 *key* 는 마찬가지로 각각 `premise`, `hypothesis`가 되게 됩니다."]},{"cell_type":"code","execution_count":13,"id":"95127c8c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"95127c8c","outputId":"26c23d84-5ead-41ba-eed9-a8d1a4e40210","executionInfo":{"status":"ok","timestamp":1687392775649,"user_tz":-540,"elapsed":2,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Sentence 1: 스페인에서 가장 인기 있는 종교 예술가인 바르톨로모 무릴로(1617 1682년)는 존경받는 성서적 성격을 편안하게 묘사했다.\n","Sentence 2: 바르톨로모 무릴로는 성서적 성격을 묘사하지 않는 것을 선호했다.\n"]}],"source":["sentence1_key, sentence2_key = (\"premise\", \"hypothesis\")\n","print(f\"Sentence 1: {datasets['train'][0][sentence1_key]}\")\n","print(f\"Sentence 2: {datasets['train'][0][sentence2_key]}\")"]},{"cell_type":"markdown","id":"fMDIRs1M4-5a","metadata":{"id":"fMDIRs1M4-5a"},"source":["이제 *key* 도 확인이 되었으니, 데이터셋에서 각 예제들을 뽑아와 토큰화 할 수 있는 함수를 아래와 같이 정의해줍니다.\n","\n","해당 함수는 모델을 훈련하기 앞서 데이터셋을 미리 토큰화 시켜놓는 작업을 위한 콜백 함수로 사용되게 됩니다.\n","\n","인자로 넣어주는 `truncation`는 모델이 입력 받을 수 있는 최대 길이 이상의 토큰 시퀀스가 들어오게 될 경우, 최대 길이 기준으로 시퀀스를 자르라는 의미를 지닙니다.\n","\n","( \\* `return_token_type_ids`는 토크나이저가 `token_type_ids`를 반환하도록 할 것인지를 결정하는 인자입니다. `transformers==4.7.0` 기준으로 `token_type_ids`가 기본적으로 반환되므로 `token_type_ids` 자체를 사용하지 않는 `RoBERTa` 모델을 활용하기 위해 해당 인자를 `False`로 설정해주도록 합니다.)"]},{"cell_type":"code","execution_count":14,"id":"2f8f8cc0","metadata":{"id":"2f8f8cc0","executionInfo":{"status":"ok","timestamp":1687392776885,"user_tz":-540,"elapsed":3,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[],"source":["def preprocess_function(examples):\n"," return tokenizer(\n"," examples[sentence1_key],\n"," examples[sentence2_key],\n"," truncation=True,\n"," return_token_type_ids=False,\n"," )"]},{"cell_type":"markdown","id":"F-qK5fgl4_6t","metadata":{"id":"F-qK5fgl4_6t"},"source":["앞서 정의한 `process_function`은 여러 개의 예제 데이터를 받을 수도 있습니다."]},{"cell_type":"code","execution_count":15,"id":"x8KrAGLpyxNO","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x8KrAGLpyxNO","outputId":"e5c82ca0-0f1c-4d05-fbe0-03db3fde1089","executionInfo":{"status":"ok","timestamp":1687392777741,"user_tz":-540,"elapsed":4,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [[0, 6385, 27135, 3676, 4316, 1513, 2259, 4786, 8560, 2179, 5957, 2777, 2200, 2391, 1088, 2388, 2200, 12, 16956, 2254, 16713, 2302, 2440, 13, 793, 7534, 2757, 2259, 8344, 2125, 4768, 2069, 5984, 2205, 2318, 6885, 2371, 2062, 18, 2, 5957, 2777, 2200, 2391, 1088, 2388, 2200, 2259, 8344, 2125, 4768, 2069, 6885, 2205, 2118, 1380, 2259, 575, 2069, 5877, 2371, 2062, 18, 2], [0, 831, 29584, 2088, 8331, 2170, 2259, 19786, 2052, 1513, 2359, 2062, 18, 2, 831, 29584, 2088, 8331, 2170, 2259, 19786, 2052, 1513, 2359, 2062, 18, 2], [0, 7032, 25657, 2155, 2079, 4132, 4471, 2252, 2052, 2359, 2414, 3752, 12489, 2179, 4075, 2259, 1750, 2318, 1567, 2359, 2062, 18, 2, 25657, 2155, 2170, 2259, 636, 3875, 904, 2178, 2062, 2119, 1039, 2073, 10290, 2116, 1513, 2062, 18, 2], [0, 1439, 2138, 3685, 16, 3698, 2073, 4426, 3759, 4076, 2069, 9962, 4897, 2200, 1238, 2886, 2088, 3659, 2079, 17600, 2170, 3618, 14544, 2502, 4192, 2069, 1410, 2015, 3694, 4071, 2138, 5651, 2371, 2062, 18, 2, 3698, 2073, 3659, 2079, 4426, 6033, 2170, 3618, 14544, 2502, 4192, 2069, 1410, 2259, 575, 2069, 4071, 2200, 1238, 2886, 2062, 18, 2], [0, 16550, 2079, 3785, 9519, 2170, 2259, 3665, 2079, 13282, 2218, 2125, 14459, 9867, 1570, 3657, 2116, 1513, 2062, 18, 2, 3665, 27135, 3676, 1751, 25353, 4605, 1570, 3657, 2259, 16550, 9519, 27135, 4257, 3598, 3606, 18, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}"]},"metadata":{},"execution_count":15}],"source":["preprocess_function(datasets[\"train\"][:5])"]},{"cell_type":"markdown","id":"a_usMC7U5AyW","metadata":{"id":"a_usMC7U5AyW"},"source":["��제 정의된 전처리 함수를 활용해 데이터셋을 미리 토큰화시키는 작업을 수행합니다.\n","\n","`datasets` 라이브러리를 통해 얻어진 `DatasetDict` 객체는 `map()` 함수를 지원하므로, 정의된 전처리 함수를 데이터셋 토큰화를 위한 콜백 함수로 `map()` 함수 인자로 넘겨주면 됩니다.\n","\n","보다 자세한 내용은 [문서](https://huggingface.co/docs/datasets/processing.html#processing-data-with-map)를 참조해주시면 됩니다."]},{"cell_type":"code","execution_count":16,"id":"f7c72286","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["962f83b5ac064e33b15231f0b2eca229","ac8f897302ba4dad9540c00c3e740566","63fefb6361164e4d80f175a5e1a4b836","5b429d358fb4420a942752ae8adb6612","4e2e73f22d2e4499903afa6e5911c1c1","dd6030bb06564da79bb6476f848bd17f","d5a3d1b38fa0403894fc9b42959061c8","a250f80e767346ecae22d4f4ff8829de","baf1738fddef4b41bbe440e9f5d8e677","39b9b50d76444dacb5ac70fa115bb606","692358fb47bf411bbeb462db8e7c1444","a2d6a2d9c9734a2a83dbcee315f05c20","4773ffb01b7845b3a9d79efd5e6f4f34","74c49cab4e174f08abd7b724ab3fcea9","e85f9c5c5a3d45b1af9efc36eed4fbf2","34149c1ec2a24fae8b22c9159ca3ca0d","f79f28c8412a4bd08eb0a924ccf6df28","8a4b4d7f57dc4faabb70b560becf541c","b95dc2414cfc447c80f9927d05bf8654","b5375adb1ec74c6b93df4d928ea96f9f","bee10b79a22d4eb081f988abf50329ac","0ce8cb88604b4be085279959fd89a3a1"]},"id":"f7c72286","outputId":"5daae89f-b045-4ba2-acd4-8316b30802f6","executionInfo":{"status":"ok","timestamp":1687392807000,"user_tz":-540,"elapsed":28436,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/360181 [00:00= 0:\n"," print('Not connected to a GPU')\n","else:\n"," print(gpu_info)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HLXwFtbJbrKa","executionInfo":{"status":"ok","timestamp":1687392807001,"user_tz":-540,"elapsed":12,"user":{"displayName":"김평진","userId":"11705489940506901507"}},"outputId":"f8e8684a-4893-420e-c9d3-4f8b00950e23"},"id":"HLXwFtbJbrKa","execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Thu Jun 22 00:13:26 2023 \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n","|-------------------------------+----------------------+----------------------+\n","| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|===============================+======================+======================|\n","| 0 NVIDIA A100-SXM... Off | 00000000:00:04.0 Off | 0 |\n","| N/A 30C P0 42W / 400W | 0MiB / 40960MiB | 0% Default |\n","| | | Disabled |\n","+-------------------------------+----------------------+----------------------+\n"," \n","+-----------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=============================================================================|\n","| No running processes found |\n","+-----------------------------------------------------------------------------+\n"]}]},{"cell_type":"code","execution_count":18,"id":"7ae93dd8","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7ae93dd8","outputId":"a657c98e-5b9e-4400-abd8-284caaa244f1","executionInfo":{"status":"ok","timestamp":1687392808357,"user_tz":-540,"elapsed":1360,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at klue/roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.weight']\n","- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at klue/roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}],"source":["num_labels = 3\n","model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels)"]},{"cell_type":"markdown","id":"ZSc4c6K5oR_R","metadata":{"id":"ZSc4c6K5oR_R"},"source":["모델을 로드할 때 발생하는 경고 문구는 두 가지 의미를 지닙니다.\n","\n","1. *Masked Language Modeling* 을 위해 존재했던 `lm_head`가 현재는 사용되지 않고 있음을 의미합니다.\n","2. 문장 분류를 위한 `classifier` 레이어를 백본 모델 뒤에 이어 붙였으나 아직 훈련이 되지 않았으므로, 학습을 수행해야 함을 의미합니다."]},{"cell_type":"markdown","id":"x6bvKcsv5CPo","metadata":{"id":"x6bvKcsv5CPo"},"source":["마지막으로 앞서 정의한 메트릭을 모델 예측 결과에 적용하기 위한 함수를 정의합니다.\n","\n","입력으로 들어오는 `eval_pred`는 [*EvalPrediction*](https://huggingface.co/transformers/internal/trainer_utils.html#transformers.EvalPrediction) 객체이며, 모델의 클래스 별 예측 값과 정답 값을 지닙니다.\n","\n","클래스 별 예측 중 가장 높은 라벨을 `argmax()`를 통해 뽑아낸 후, 정답 라벨과 비교를 하게 됩니다."]},{"cell_type":"code","execution_count":19,"id":"cbfb67d3","metadata":{"id":"cbfb67d3","executionInfo":{"status":"ok","timestamp":1687392808357,"user_tz":-540,"elapsed":3,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[],"source":["def compute_metrics(eval_pred):\n"," predictions, labels = eval_pred\n"," predictions = np.argmax(predictions, axis=1)\n"," return metric.compute(predictions=predictions, references=labels)"]},{"cell_type":"markdown","id":"aU_dmMAVE_FA","metadata":{"id":"aU_dmMAVE_FA"},"source":["이제 앞서 정의한 정보들을 바탕으로 `transformers`에서 제공하는 *Trainer* 객체를 활용하기 위한 인자 관리 클래스를 초기화합니다.\n","\n","`metric_name`은 앞서 얻어진 메트릭 함수를 활용했을 때, 아래와 같이 `dict` 형식으로 결과 값이 반환되는데 여기서 우리가 사용할 *key* 를 정의해준다고 생각하시면 됩니다.\n","\n","```python\n",">>> metric.compute(predictions=fake_preds, references=fake_labels)\n","{'accuracy': 0.515625}\n","```\n","\n","각 인자에 대한 자세한 설명은 [문서](https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments)에서 참조해주시면 됩니다."]},{"cell_type":"code","execution_count":20,"id":"864b6408","metadata":{"id":"864b6408","executionInfo":{"status":"ok","timestamp":1687392808358,"user_tz":-540,"elapsed":3,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[],"source":["metric_name = \"accuracy\"\n","\n","args = TrainingArguments(\n"," \"test-nli\",\n"," evaluation_strategy=\"epoch\",\n"," learning_rate=2e-5,\n"," per_device_train_batch_size=batch_size,\n"," per_device_eval_batch_size=batch_size,\n"," num_train_epochs=5,\n"," weight_decay=0.01,\n"," load_best_model_at_end=False,\n"," save_strategy=\"epoch\",\n"," save_total_limit = 10,\n"," metric_for_best_model=metric_name,\n",")"]},{"cell_type":"markdown","id":"PcvI6ZHe3Ty6","metadata":{"id":"PcvI6ZHe3Ty6"},"source":["이제 로드한 모델, 인자 관리 클래스, 데이터셋 등을 *Trainer* 클래스를 초기화에 넘겨주도록 합니다.\n","\n","(TIP: Q: 이미 `encoded_datasets`을 만드는 과정에 토큰화가 이루어졌는데 토크나이저를 굳이 넘겨주는 이유가 무엇인가요?,
A: 토큰화는 이루어졌지만 학습 과정 시, 데이터를 배치 단위로 넘겨주는 과정에서 배치에 포함된 가장 긴 시퀀스 기준으로 `truncation`을 수행하고 최대 길이 시퀀스 보다 짧은 시퀀스들은 그 길이만큼 `padding`을 수행��주기 위함입니다.)"]},{"cell_type":"code","execution_count":21,"id":"c91650f2","metadata":{"id":"c91650f2","executionInfo":{"status":"ok","timestamp":1687392810200,"user_tz":-540,"elapsed":1845,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[],"source":["trainer = Trainer(\n"," model,\n"," args,\n"," train_dataset=encoded_datasets[\"train\"],\n"," eval_dataset=encoded_datasets[\"test\"],\n"," tokenizer=tokenizer,\n"," compute_metrics=compute_metrics,\n",")"]},{"cell_type":"markdown","id":"KImxQi1ZFAO4","metadata":{"id":"KImxQi1ZFAO4"},"source":["이제 정의된 *Trainer* 객체를 다음과 같이 훈련시킬 수 있습니다.\n","\n","에폭이 지남에 따라 *Loss* 는 떨어지고, 앞서 선정한 메트릭인 *Accuracy* 는 증가하는 것을 확인할 수 있습니다."]},{"cell_type":"code","execution_count":22,"id":"73261f8e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":345},"id":"73261f8e","outputId":"4a1c1ccf-61b5-41df-f28d-b8b979ec5673","executionInfo":{"status":"ok","timestamp":1687401441835,"user_tz":-540,"elapsed":8631638,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n"," warnings.warn(\n","You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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EpochTraining LossValidation LossAccuracy
10.5227000.5167030.799305
20.4273000.5038210.810275
30.3255000.5382720.811599
40.2262000.6216560.810924
50.1672000.7363670.811424

"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=56280, training_loss=0.3434749272501714, metrics={'train_runtime': 8631.4556, 'train_samples_per_second': 208.644, 'train_steps_per_second': 6.52, 'total_flos': 9.673161506543254e+16, 'train_loss': 0.3434749272501714, 'epoch': 5.0})"]},"metadata":{},"execution_count":22}],"source":["trainer.train()"]},{"cell_type":"markdown","id":"avS6mBu8779g","metadata":{"id":"avS6mBu8779g"},"source":["*Trainer* 는 학습을 마치게 되면, `load_best_model_at_end=True` 인자에 따라 메트릭 기준 가장 좋은 성능을 보였던 체크포인트를 로드하게 됩니다.\n","\n","본 노트북에서는 마지막 에폭 때 가장 좋은 성능을 얻었기에 `evaluate`를 수행해도 같은 결과가 나오겠습니다."]},{"cell_type":"code","execution_count":23,"id":"58239e17","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":149},"id":"58239e17","outputId":"a8a7458a-339e-4bd8-8843-87e59c5005ac","executionInfo":{"status":"ok","timestamp":1687401508734,"user_tz":-540,"elapsed":59019,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","

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\n"," "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 0.7363672256469727,\n"," 'eval_accuracy': 0.8114240023987407,\n"," 'eval_runtime': 57.0187,\n"," 'eval_samples_per_second': 701.892,\n"," 'eval_steps_per_second': 21.94,\n"," 'epoch': 5.0}"]},"metadata":{},"execution_count":23}],"source":["trainer.evaluate()"]},{"cell_type":"code","source":["!pip install huggingface_hub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hSqW3lYj8-40","executionInfo":{"status":"ok","timestamp":1687401526405,"user_tz":-540,"elapsed":4701,"user":{"displayName":"김평진","userId":"11705489940506901507"}},"outputId":"3b31f9fa-d9b1-4a3a-8f63-2ed8e3edeba0"},"id":"hSqW3lYj8-40","execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.15.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.0)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.4.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.27.1)\n","Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.65.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (1.26.15)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2022.12.7)\n","Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.12)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n"]}]},{"cell_type":"code","source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["67ed315f5d8b4442bb245274d15fdc34","3f4ad6f61c9a4aea935de88a01be95d4","58dad53747844d389b2c65c3b8e63131","b8d3cc3e90ca4432966d6ff9a3a185ab","47001cb3f6c84f35863d5158aed9c9e5","e3602b86556447d1ad282b6282e67630","5e05a392f5ce42e69173a6e664f42ace","78fe6afc5cf24fe3b6ccc6c2a13e8adb","9e7ecb5fc0ca4d5b8d3d88387ce8c651","4db43a8501e346ffbdf62631e48626a4","c075b208c4a04e4f8cff425a8822f387","18b790ba561748a6b36b7b372acec93f","6cafd93e2ea74e60bbe6ce0c9574774b","d4a3e2d58efb465e8cdfc1b476c5e824","4f824faf60a0424490e864369c92929f","9e40f85cbab5434b8f149570d6ad744e","d73fcbffa65747548d0ef1bef71f8f0c","f2f3e7452e324e25b81a208f28e85470","4a3588ce965540109fcf92d2f45891ce","d71fbe414a5146f29edb5c6d190d8d1b","2771d4fd0398428896c474790aed6728","1cdba0dc698447fc98545136b1550070","f455098f355d4b4293dbdee5d096d3b5","4745ece55eb54be39839908434a9defc","dee705891e5e46a7bd5842aee8df417e","bc951afd648146cbb3a6ab8d840c2afb","3dcc5114b3d142cba5b2a75fce44fb54","322c06ca624642788ba494603da29a69","17f384f492734e848d1fd1afc541f534","5aeb1186a45e48b697251f4eb696a018","8765adbd4f584fe1829bc2d33df511f3","576bcb0f43e24e668437061fe1d02eec"]},"id":"k3Kj2tswc73K","executionInfo":{"status":"ok","timestamp":1687401529173,"user_tz":-540,"elapsed":12,"user":{"displayName":"김평진","userId":"11705489940506901507"}},"outputId":"2f00af12-489c-41e1-f5e5-833c0e624b4a"},"id":"k3Kj2tswc73K","execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='
\u001b[0m:\u001b[94m1\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m3716\u001b[0m in \u001b[92mpush_to_hub\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3713 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# If a user calls manually `push_to_hub` with `self.args.push_to_hub = False`, w\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3714 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# it might fail.\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3715 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m \u001b[96mhasattr\u001b[0m(\u001b[96mself\u001b[0m, \u001b[33m\"\u001b[0m\u001b[33mrepo\u001b[0m\u001b[33m\"\u001b[0m): \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m3716 \u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.init_git_repo() \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3717 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3718 \u001b[0m\u001b[2m│ │ \u001b[0mmodel_name = kwargs.pop(\u001b[33m\"\u001b[0m\u001b[33mmodel_name\u001b[0m\u001b[33m\"\u001b[0m, \u001b[94mNone\u001b[0m) \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3719 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m model_name \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m \u001b[95mand\u001b[0m \u001b[96mself\u001b[0m.args.should_save: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m3571\u001b[0m in \u001b[92minit_git_repo\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3568 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[2m# Make sure the repo exists.\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3569 \u001b[0m\u001b[2m│ │ \u001b[0mcreate_repo(repo_name, token=\u001b[96mself\u001b[0m.args.hub_token, private=\u001b[96mself\u001b[0m.args.hub_private_ \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3570 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m3571 \u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.repo = Repository(\u001b[96mself\u001b[0m.args.output_dir, clone_from=repo_name, token=\u001b[96msel\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3572 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mEnvironmentError\u001b[0m: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3573 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.args.overwrite_output_dir \u001b[95mand\u001b[0m at_init: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m3574 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[2m# Try again after wiping output_dir\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/\u001b[0m\u001b[1;33m_validators.py\u001b[0m:\u001b[94m118\u001b[0m in \u001b[92m_inner_fn\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m115 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m check_use_auth_token: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m116 \u001b[0m\u001b[2m│ │ │ \u001b[0mkwargs = smoothly_deprecate_use_auth_token(fn_name=fn.\u001b[91m__name__\u001b[0m, has_token=ha \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m117 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m118 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m fn(*args, **kwargs) \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m119 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m120 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mreturn\u001b[0m _inner_fn \u001b[2m# type: ignore\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m121 \u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/\u001b[0m\u001b[1;33mrepository.py\u001b[0m:\u001b[94m516\u001b[0m in \u001b[92m__init__\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 513 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.huggingface_token = HfFolder.get_token() \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 514 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 515 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m clone_from \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 516 \u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.clone_from(repo_url=clone_from) \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 517 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 518 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m is_git_repo(\u001b[96mself\u001b[0m.local_dir): \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 519 \u001b[0m\u001b[2m│ │ │ │ \u001b[0mlogger.debug(\u001b[33m\"\u001b[0m\u001b[33m[Repository] is a valid git repo\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/\u001b[0m\u001b[1;33m_validators.py\u001b[0m:\u001b[94m118\u001b[0m in \u001b[92m_inner_fn\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m115 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m check_use_auth_token: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m116 \u001b[0m\u001b[2m│ │ │ \u001b[0mkwargs = smoothly_deprecate_use_auth_token(fn_name=fn.\u001b[91m__name__\u001b[0m, has_token=ha \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m117 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m118 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m fn(*args, **kwargs) \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m119 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m120 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mreturn\u001b[0m _inner_fn \u001b[2m# type: ignore\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m121 \u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/huggingface_hub/\u001b[0m\u001b[1;33mrepository.py\u001b[0m:\u001b[94m680\u001b[0m in \u001b[92mclone_from\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 677 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 678 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[2m# Check if the folder is the root of a git repository\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 679 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m is_git_repo(\u001b[96mself\u001b[0m.local_dir): \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 680 \u001b[2m│ │ │ │ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mEnvironmentError\u001b[0m( \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 681 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mTried to clone a repository in a non-empty folder that isn\u001b[0m\u001b[33m'\u001b[0m\u001b[33mt\u001b[0m\u001b[33m\"\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 682 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0m\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m a git repository (\u001b[0m\u001b[33m'\u001b[0m\u001b[33m{\u001b[0m\u001b[96mself\u001b[0m.local_dir\u001b[33m}\u001b[0m\u001b[33m'\u001b[0m\u001b[33m). If you really want to\u001b[0m\u001b[33m\"\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m│\u001b[0m \u001b[2m 683 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0m\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m do this, do it manually:\u001b[0m\u001b[33m\\n\u001b[0m\u001b[33m cd \u001b[0m\u001b[33m{\u001b[0m\u001b[96mself\u001b[0m.local_dir\u001b[33m}\u001b[0m\u001b[33m && git init\u001b[0m\u001b[33m\"\u001b[0m \u001b[31m│\u001b[0m\n","\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n","\u001b[1;91mOSError: \u001b[0mTried to clone a repository in a non-empty folder that isn't a git repository \u001b[1m(\u001b[0m\u001b[32m'/content/test-nli'\u001b[0m\u001b[1m)\u001b[0m. If \n","you really want to do this, do it manually:\n"," cd \u001b[35m/content/\u001b[0m\u001b[95mtest-nli\u001b[0m && git init && git remote add origin && git pull origin main\n"," or clone repo to a new folder and move your existing files there afterwards.\n"],"text/html":["
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n","│ in <cell line: 1>:1                                                                              │\n","│                                                                                                  │\n","│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:3716 in push_to_hub              │\n","│                                                                                                  │\n","│   3713 │   │   # If a user calls manually `push_to_hub` with `self.args.push_to_hub = False`, w  │\n","│   3714 │   │   # it might fail.                                                                  │\n","│   3715 │   │   if not hasattr(self, \"repo\"):                                                     │\n","│ ❱ 3716 │   │   │   self.init_git_repo()                                                          │\n","│   3717 │   │                                                                                     │\n","│   3718 │   │   model_name = kwargs.pop(\"model_name\", None)                                       │\n","│   3719 │   │   if model_name is None and self.args.should_save:                                  │\n","│                                                                                                  │\n","│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:3571 in init_git_repo            │\n","│                                                                                                  │\n","│   3568 │   │   # Make sure the repo exists.                                                      │\n","│   3569 │   │   create_repo(repo_name, token=self.args.hub_token, private=self.args.hub_private_  │\n","│   3570 │   │   try:                                                                              │\n","│ ❱ 3571 │   │   │   self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=sel  │\n","│   3572 │   │   except EnvironmentError:                                                          │\n","│   3573 │   │   │   if self.args.overwrite_output_dir and at_init:                                │\n","│   3574 │   │   │   │   # Try again after wiping output_dir                                       │\n","│                                                                                                  │\n","│ /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:118 in _inner_fn    │\n","│                                                                                                  │\n","│   115 │   │   if check_use_auth_token:                                                           │\n","│   116 │   │   │   kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=ha   │\n","│   117 │   │                                                                                      │\n","│ ❱ 118 │   │   return fn(*args, **kwargs)                                                         │\n","│   119 │                                                                                          │\n","│   120 │   return _inner_fn  # type: ignore                                                       │\n","│   121                                                                                            │\n","│                                                                                                  │\n","│ /usr/local/lib/python3.10/dist-packages/huggingface_hub/repository.py:516 in __init__            │\n","│                                                                                                  │\n","│    513 │   │   │   self.huggingface_token = HfFolder.get_token()                                 │\n","│    514 │   │                                                                                     │\n","│    515 │   │   if clone_from is not None:                                                        │\n","│ ❱  516 │   │   │   self.clone_from(repo_url=clone_from)                                          │\n","│    517 │   │   else:                                                                             │\n","│    518 │   │   │   if is_git_repo(self.local_dir):                                               │\n","│    519 │   │   │   │   logger.debug(\"[Repository] is a valid git repo\")                          │\n","│                                                                                                  │\n","│ /usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py:118 in _inner_fn    │\n","│                                                                                                  │\n","│   115 │   │   if check_use_auth_token:                                                           │\n","│   116 │   │   │   kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=ha   │\n","│   117 │   │                                                                                      │\n","│ ❱ 118 │   │   return fn(*args, **kwargs)                                                         │\n","│   119 │                                                                                          │\n","│   120 │   return _inner_fn  # type: ignore                                                       │\n","│   121                                                                                            │\n","│                                                                                                  │\n","│ /usr/local/lib/python3.10/dist-packages/huggingface_hub/repository.py:680 in clone_from          │\n","│                                                                                                  │\n","│    677 │   │   │   else:                                                                         │\n","│    678 │   │   │   │   # Check if the folder is the root of a git repository                     │\n","│    679 │   │   │   │   if not is_git_repo(self.local_dir):                                       │\n","│ ❱  680 │   │   │   │   │   raise EnvironmentError(                                               │\n","│    681 │   │   │   │   │   │   \"Tried to clone a repository in a non-empty folder that isn't\"    │\n","│    682 │   │   │   │   │   │   f\" a git repository ('{self.local_dir}'). If you really want to\"  │\n","│    683 │   │   │   │   │   │   f\" do this, do it manually:\\n cd {self.local_dir} && git init\"    │\n","╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n","OSError: Tried to clone a repository in a non-empty folder that isn't a git repository ('/content/test-nli'). If \n","you really want to do this, do it manually:\n"," cd /content/test-nli && git init && git remote add origin && git pull origin main\n"," or clone repo to a new folder and move your existing files there afterwards.\n","
\n"]},"metadata":{}}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6cMuYwkPdQG9","executionInfo":{"status":"ok","timestamp":1687401591361,"user_tz":-540,"elapsed":26936,"user":{"displayName":"김평진","userId":"11705489940506901507"}},"outputId":"1b638418-bd6c-4e76-84d3-9a39993987f0"},"id":"6cMuYwkPdQG9","execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["# push to hub는 에러가 뜨기에 직접 코랩 로컬에 저장 후 다운로드\n","trainer.save_model(\"/content/drive/MyDrive/국민은행과제\")"],"metadata":{"id":"3zks-x8Fc7sZ","executionInfo":{"status":"ok","timestamp":1687401620121,"user_tz":-540,"elapsed":1250,"user":{"displayName":"김평진","userId":"11705489940506901507"}}},"id":"3zks-x8Fc7sZ","execution_count":30,"outputs":[]},{"cell_type":"markdown","id":"stk6rOUyFJuM","metadata":{"id":"stk6rOUyFJuM"},"source":["지금까지 `transformers`를 라이브러리 내 문장 분류 모델을 학습하는 과정을 KLUE NLI 데이터셋을 통해 알아보았습니다.\n","\n","본 노트북을 통해 습득한 지식이 여러분의 업무와 학습에 도움이 되었으면 좋겠습니다.\n","\n","```\n","허 훈 (huffonism@gmail.com)\n","```"]},{"cell_type":"markdown","id":"SkNpAqSJ78oM","metadata":{"id":"SkNpAqSJ78oM"},"source":["APPENDIX: 앞서 학습된 모델을 HuggingFace 모델 허브에 업로드하였으니, 아래 예제와 같이 `pipeline` 함수를 통해 사용이 가능합니다.\n","\n","먼저 `text-classification` 태스크로 파이프라인 객체를 초기화합니다.\n","\n","( \\* `return_all_scores`는 모델이 입력 문장에 대해 측정한 각 라벨에 대한 확률 값을 모두 보여줄 것인지를 결정하는 인자입니다.)"]},{"cell_type":"code","execution_count":null,"id":"htUZ0ws19Ksk","metadata":{"id":"htUZ0ws19Ksk"},"outputs":[],"source":["from transformers import pipeline\n","\n","classifier = pipeline(\n"," \"text-classification\",\n"," model=\"Huffon/klue-roberta-base-nli\",\n"," return_all_scores=True,\n",")"]},{"cell_type":"markdown","id":"2uW7UQgBFBPw","metadata":{"id":"2uW7UQgBFBPw"},"source":["NLI는 두 문장의 페어를 입력 값으로 주어야 하기 때문에 구분자 스페셜 토큰을 두 문장 사이에 넣어주기 위해 토크나이저 객체를 로드합니다.\n","\n","`[SEP]` 문자열을 하드코딩하여 넣어줄 수도 있겠지만, 스페셜 토큰은 토크나이저 마다 다르게 정의되므로 다른 모델을 활용할 때 보다 코드를 재사용할 수 있도록 토크나이저의 `sep_token` 프로퍼티에 접근하는 방식으로 코드를 작성합니다."]},{"cell_type":"code","execution_count":null,"id":"UoXTmep0Dcwg","metadata":{"id":"UoXTmep0Dcwg"},"outputs":[],"source":["tokenizer = AutoTokenizer.from_pretrained(\"Huffon/klue-roberta-base-nli\")"]},{"cell_type":"code","execution_count":null,"id":"MuOQhiVPDiMq","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"MuOQhiVPDiMq","outputId":"6c7f4d51-ceb9-4650-e5d6-19d4f6b604ac"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'[SEP]'"]},"execution_count":87,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["tokenizer.sep_token"]},{"cell_type":"markdown","id":"iCP9OgWHFDLG","metadata":{"id":"iCP9OgWHFDLG"},"source":["`sep_token`으로 두 문장을 이어 하나의 입력 값으로 파이프라인에 넘겨줍니다.\n","\n","입력된 문장에 대해 모델이 각 라벨에 대해 어떤 확률을 가지고 예측했는지를 확인할 수 있습니다."]},{"cell_type":"code","execution_count":null,"id":"tVCGIVQDChcQ","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tVCGIVQDChcQ","outputId":"f2703b6f-90c0-4107-cd6b-9f2e265713cc"},"outputs":[{"data":{"text/plain":["[[{'label': 'ENTAILMENT', 'score': 0.9865273833274841},\n"," {'label': 'NEUTRAL', 'score': 0.013215206563472748},\n"," {'label': 'CONTRADICTION', 'score': 0.0002573762903921306}]]"]},"execution_count":105,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["classifier(f\"흡연하려면 발코니 있는 방을 선택하면 됩니다. {tokenizer.sep_token} 흡연자분들은 발코니가 있는 방이면 발코니에서 흡연이 가능합니다.\")"]},{"cell_type":"code","execution_count":null,"id":"Ud2kuxlNCmGI","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ud2kuxlNCmGI","outputId":"7d53c878-9fb1-4e05-da40-ab06df811a1f"},"outputs":[{"data":{"text/plain":["[[{'label': 'ENTAILMENT', 'score': 0.0002685467479750514},\n"," {'label': 'NEUTRAL', 'score': 0.0006742384284734726},\n"," {'label': 'CONTRADICTION', 'score': 0.9990572333335876}]]"]},"execution_count":106,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["classifier(f\"호스트분은 영어밖에 못하십니다. {tokenizer.sep_token} 호스트분도 엄청 친절하시고 영어도 잘하십니다.\")"]},{"cell_type":"markdown","id":"Uvx8PPTsrPyC","metadata":{"id":"Uvx8PPTsrPyC"},"source":["검증 데이터에 존재하는 임의의 문장 페어를 입력해보니, 우리가 원하던 결과를 얻을 수 있었습니다.\n","\n","*(cf. NLI 데이터에 대해 학습된 모델을 활용해 Zero-shot Classification을 수행하는 예제가 궁금하신 분들은 해당 [노��북](https://colab.research.google.com/github/Huffon/klue-transformers-tutorial/blob/master/zero_shot_classification.ipynb)을 참조해주세요.)*"]}],"metadata":{"colab":{"provenance":[],"machine_shape":"hm","gpuType":"A100"},"kernelspec":{"display_name":"Python 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