[NLP] 문서 분류 모델 실전 투입
자, 그럼 학습을 마친 모델을 어떻게 사용할까?
본 파일은 이기창님의 ‘Do it! 자연어 처리’에 기초하여 작성되었다! :)
학습 마친 모델을 실전 투입하기
이번 실습에서는 학습을 마친 문서 분류 모델을 가지고 웹 서비스를 만든다.
문장을 받아 해당 문장이 긍정인지 부정인지 답변하는 웹 서비스로, 문장을 토큰화한 뒤 모델 입력값으로 만들고 이를 모델에 입력해 [해당 문장이 긍정일 확률, 해당 문장이 부정일 확률 ]을 계산하게 만든다. 이후 약간의 후처리 과정을 거쳐 응답하게 만드는 방식이다.
웹 서비스란 네트워크에서 컴퓨터 간에 상호작용을 하기 위해 만들어진 소프트웨어 시스템이다. 본 노트에서는 원격 사용자가 보낸 문장을 수신해 해당 문장이 긍정인지 부정인지 응답을 만들고 이 응답을 원격 사용자에게 전달하는 웹 서비스를 만드는 것이다.
1. 환경 설정하기
의존성 패키지 설치
pip 명령어를 통해 의존성 있는 패키지를 설치한다.
code 2-0
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!pip install ratsnlp
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구글 드라이브 연동
모델 체크포인트는 구글 드라이브에 저장해 두었다. 코랩 노트북과 자신의 구글 드라이브를 연동한다.
code 2-1
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from google.colab import drive
drive.mount('/gdrive', force_remount=True)
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Mounted at /gdrive
인퍼런스 설정
각종 인자( 모델 하이퍼파라미터(hyperparameter)와 저장 위치 등 )를 설정한다.
code 2-2
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from ratsnlp.nlpbook.classification import ClassificationDeployArguments
args = ClassificationDeployArguments(
pretrained_model_name="beomi/kcbert-base",
downstream_model_dir="/gdrive/My Drive/nlpbook/checkpoint-doccls",
max_seq_length=128,
)
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downstream_model_checkpoint_fpath: /gdrive/My Drive/nlpbook/checkpoint-doccls/epoch=1-val_loss=0.28.ckpt
각 인자의 역할과 내용은 다음과 같다.
- pretrained_model_name:
training_section.ipynb에서 적용한pretrained_model_name(단, 해당 모델은 허깅페이스 라이브러리에 등록되어 있어야 한다.) - downstream_model_dir:
training_section.ipynb에서 파인튜닝한 모델의 체크포인트 저장 위치(확장자가ckpt인 파일이 하나 이상 있어야 한다.) - max_seq_length: 토큰 기준 입력 문장 최대 길이. 아무것도 입력하지 않으면 128.
2. 토크나이저 및 모델 불러오기
토크나이저 로드
code 2-3을 실행해 토크나이저를 초기화 한다.
code 2-3
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from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained(
args.pretrained_model_name,
do_lower_case=False,
)
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체크포인트 로드
code 2-4는 training_section.ipynb에서 파인튜닝한 모델의 체크포인트를 읽어들인다.
code 2-4
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import torch
fine_tuned_model_ckpt = torch.load(
args.downstream_model_checkpoint_fpath,
map_location=torch.device("cpu"),
)
BERT 설정 로드 및 BERT 모델 초기화
code 2-5는 training_section.ipynb의 파인튜닝 때 사용한 pretrained_model_name에 해당하는 모델의 설정값들을 읽어들인다.
이어서 code 2-6을 실행하면 해당 설정값대로 BERT 모델을 초기화 한다.
code 2-5
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from transformers import BertConfig
pretrained_model_config = BertConfig.from_pretrained(
args.pretrained_model_name,
num_labels=fine_tuned_model_ckpt["state_dict"]["model.classifier.bias"].shape.numel(),
)
code 2-6
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from transformers import BertForSequenceClassification
model = BertForSequenceClassification(pretrained_model_config)
체크포인트 주입하기
code 2-7은 초기화한 BERT모델에 체크포인트(fine_tuned_model_ckpt)를 주입한다.
code 2-7
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model.load_state_dict({k.replace("model.",""): v for k, v in fine_tuned_model_ckpt['state_dict'].items()})
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<All keys matched successfully>
평가모드로 전환
이어서 code 2-8을 실행하면 모델이 평가모드로 전환되게 된다. 드롭아웃 등 학습 때만 사용하는 기법들을 무효화하는 역할을 한다.
code 2-8
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model.eval()
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BertForSequenceClassification(
(bert): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(30000, 768, padding_idx=0)
(position_embeddings): Embedding(300, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(1): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(2): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(3): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(4): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(5): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(6): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(7): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(8): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(9): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(10): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(11): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
(dropout): Dropout(p=0.1, inplace=False)
(classifier): Linear(in_features=768, out_features=2, bias=True)
)
3. 모델 출력값 만들고 후처리 하기
code 2-9는 인퍼런스 과정을 정의한 함수이다. 문장에 토큰화를 수행한 뒤 input_ids, attention_mask, token_type_ids를 만든다. 이들 입력값을 파이토치의 텐서 자료형으로 변환한 뒤 모델에 입력한다. 모델 출력값(outputs.logits)은 소프트맥스 함수 적용 이전의 로짓(logit)형태인데, 여기에 소프트맥스 함수를 써서 모델 출력을 ‘[부정일 확률, 긍정일 확률]’로 바꾼다.
마지막으로 모델 출력을 약간 후처리 하여 예측 확률의 최댓값이 부정 위치일 때 해당 문장이 ‘부정(negative)’, 반대일 때는 ‘긍정(positive)’이 되도록 pred값을 만든다.
code 2-9
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def inference_fn(sentence):
# 문장을 토큰화한 뒤 input_id, attention_masks, token_type_ids 만들기
inputs = tokenizer(
[sentence],
max_lenght=args.max_seq_length,
padding="max_length",
truncation=True,
)
with torch.no_grad():
# 모델 계산하기
outputs = model(**{k: torch.tensor(v) for k, v in inputs.items()}) # {}안 = inputs를 파이토치 텐서로 바꾸기
# 로짓에 소프트 맥스 취하기
prob = outputs.logits.softmax(dim=1)
# 긍정/부정 확률을 소수점 4자리로 반올림
positive_prob = round(prob[0][1].item(), 4)
negative_prob = round(prob[0][0].item(), 4)
# 예측 확률의 최댓값 위치에 따라 pred 만들기
pred = "긍정 (positive)" if torch.argmax(prob) == 1 else "부정 (negative)"
return {
'sentence' : sentence,
'prediction': pred,
'positive_data': f"긍정 {positive_prob}",
'negative_data': f"부정 {negative_prob}",
'positive_width': f"{positive_prob * 100}%",
'negative_width': f"{negative_prob * 100}%",
}
code 2-9에서 positive_width, negative_width는 웹 페이지에서 긍정/부정 막대의 길이를 조정하려는 것이므로 크게 신경쓰지 않아도 된다.
4. 웹 서비스 시작하기
웹 서비스 만들기 준비
ngrok은 코랩 로컬에서 실행 중인 웹서비스를 안전하게 외부에서 접근 가능하도록 해주는 도구이다. ngrok을 실행하려면 회원가입 후 로그인을 한 뒤 이곳에 접속해 인증토큰(authtoken)을 확인해야 한다.
예를 들어 확인된 authtoken이 test123이라면 다음과 같이 실행 된다.
** !mkdir /root/.ngrok2 && echo “authtoken: test123” > /root/.ngrok2/ngrok.yml**
code 2-10
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!mkdir /root/.ngrok2 && echo "authtoken: (여기 채우세요))" > /root/.ngrok2/ngrok.yml
웹 서비스 시작하기
code 2-9에서 정의한 인퍼런스 함수 inference_fn을 가지고 code 2-11을 실행하면 플라스크(flask)라는 파이썬 라이브러리의 도움을 받아 웹 서비스를 띄울 수 있다.
code 2-11
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from ratsnlp.nlpbook.classification import get_web_service_app
app = get_web_service_app(inference_fn)
app.run()
웹 사이트의 형태는 다음과 같다.
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