[NLP] 문장 쌍 분류 모델 실전 투입
자, 그럼 학습을 마친 모델을 어떻게 사용할까?
본 파일은 이기창님의 ‘Do it! 자연어 처리’에 기초하여 작성되었음을 미리 알려드립니다! :)
학습 마친 모델을 실전 투입하기
학습을 마친 문장 쌍 분류 모델을 인퍼런스하는 과정을 실습해본다. 이번 실습에서 만드는 웹 서비스의 개념도는 아래 그림 1과 같다.
그림 1. 문장 쌍 분류 웹 서비스
전제와 가설 문장을 받아 답변하는 웹 서비스이다. 전제와 가설 각각을 토큰화, 인덱싱한 뒤 모델 입력값으로 만들고 이를 모델에 넣어
[전제에 대해 가설이 참일 확률, 전제에 대해 가설이 거짓일 확률, 전제에 대해 가설이 중립일 확률] 을 계산한다.
이후 약간의 후처리 과정을 거쳐 응답하는 방식이다.
전제와 가설을 검증하는 웹 서비스 만들기
1. 환경 설정하기
의존성 패키지 설치
pip 명령어를 통해 의존성있는 패키지를 설치한다.
code 4-0
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!pip install ratsnlp
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구글 드라이브 연동
학습한 모델의 체크포인트는 구글 드라이브에 저장해 두었으므로, code 4-1을 실행하여 코랩 노트북과 자신의 구글 드라이브를 연동한다.
code 4-1
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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 인퍼런스 설정을 수행한다. pretrained_model_name과 max_seq_length, downstream_model_dir 모두 앞 트레인에서 적용한 그대로 입력하여야 한다.
code 4-2
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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-paircls",
max_seq_length=64,
)
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downstream_model_checkpoint_fpath: /gdrive/My Drive/nlpbook/checkpoint-paircls/epoch=1-val_loss=0.82.ckpt
2. 토크나이저 및 모델 불러오기
토크나이저 로드
code 4-3을 실행해 토크나이저를 초기화한다.
code 4-3
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from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained(
args.pretrained_model_name,
do_lower_case=False,
)
체크포인트 로드
code 4-4는 pair_classification_train.ipynb에서 파인튜닝한 모델의 체크포인트를 읽어 들인다.
code 4-4
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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 4-5는 pair_classification_train.ipynb의 파인튜닝 때 사용한 pretrained_model_name에 해당하는 모델의 설정값들을 읽어들이며, code 4-6을 실행하면 해당 값대로 BERT 모델을 초기화 한다.
code 4-5
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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 4-6
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from transformers import BertForSequenceClassification
model = BertForSequenceClassification(pretrained_model_config)
체크포인트 주입하기
code 4-7은 초기화한 BERT모델에 code 4-4의 체크포인트를 주입한다
code 4-7
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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>
평가 모드로 전환
이어서 code 4-8을 실행하면 모델이 평가모드로 전환되게 된다. 드롭아웃 등 학습 때만 사용하는 기법들을 무효화하는 역할을 한다.
code 4-8
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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=3, bias=True)
)
3. 모델 출력값 만들고 후처리 하기
code 4-9는 인퍼런스 과정을 정의한 함수이다. 전제(premise)와 가설(hypothesis)을 입력받아 각각 토큰화, 인덱싱을 수행한 뒤 input_ids, attention_mask, token_type_ids를 만든다. 이들 압력값을 파이토치 텐서 자료형으로 변환한 뒤 모델에 입력한다.
인퍼런스 함수
code 4-9
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def inference_fn(premise, hypothesis):
# 전제와 가설을 모델 입력값으로 만들기
inputs = tokenizer(
[(premise, hypothesis)],
max_length=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 소프트맥스 취하기
prob = outputs.logits.softmax(dim=1)
# 확률을 소수점 두 자리에서 반올림
entailment_prob = round(prob[0][0].item(), 2)
contradiction_prob = round(prob[0][1].item(), 2)
neutral_prob = round(prob[0][2].item(), 2)
# 예측 확률의 최댓값 위치에 따라 pred 만들기
if torch.argmax(prob) == 0:
pred = "참 (entailment)"
elif torch.argmax(prob) == 1:
pred = "거짓 (contradiction)"
else:
pred = "중립 (neutral)"
return {
'premise': premise,
'hypothesis': hypothesis,
'prediction': pred,
'entailment_data': f"참 {entailment_prob}",
'contradiction_data': f"거짓 {contradiction_prob}",
'neutral_data': f"중립 {neutral_prob}",
'entailment_width': f"{entailment_prob * 100}%",
'contradiction_width': f"{contradiction_prob * 100}%",
'neutral_width': f"{neutral_prob * 100}%"
}
모델 출력값(output.logits)은 소프트맥스 함수 적용 이전의 로짓 형태이다. 여기에 소프트맥스 함수를 써서 모델 출력을 확률 형태로 바꾼다. 그리고 약간 후처리하여 예측 확률의 최댓값이 참 위치(0)일 경우 해당 문장이 ‘참 (entailment)’, 거짓 위치(1)일 경우 ‘거짓 (contradiction)’, 중립 위치(2)일 경우 ‘중립 (neutral)‘이 되도록 pred 값을 만든다.
code 4-9에서 entailment_width, contradiction_width, neutral_width는 웹 페이지에서 참, 거짓, 중립 막대 길이를 조정하는 정보이므로 크게 신경 쓰지 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 4-10
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!mkdir /root/.ngrok2 && echo "authtoken: (여기 채우세요)" > /root/.ngrok2/ngrok.yml
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mkdir: cannot create directory ‘/root/.ngrok2’: File exists
웹 서비스 시작하기
code 4-9에서 정의한 인퍼런스 함수 inference_fn을 가지고 code 4-11을 실행하면 웹 서비스를 띄울 수 있다. 파이썬의 플라스크를 활용한 앱이다.
code 4-11
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from ratsnlp.nlpbook.paircls import get_web_service_app
app = get_web_service_app(inference_fn)
app.run()
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* Serving Flask app "ratsnlp.nlpbook.paircls.deploy" (lazy loading)
* Environment: production
[31m WARNING: This is a development server. Do not use it in a production deployment.[0m
[2m Use a production WSGI server instead.[0m
* Debug mode: off
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
* Running on http://0163-35-238-180-140.ngrok.io
* Traffic stats available on http://127.0.0.1:4040
127.0.0.1 - - [04/Mar/2022 09:14:48] "[37mGET / HTTP/1.1[0m" 200 -
127.0.0.1 - - [04/Mar/2022 09:14:49] "[33mGET /favicon.ico HTTP/1.1[0m" 404 -
127.0.0.1 - - [04/Mar/2022 09:14:49] "[37mGET / HTTP/1.1[0m" 200 -
127.0.0.1 - - [04/Mar/2022 09:15:01] "[37mPOST /api HTTP/1.1[0m" 200 -
웹사이트 형태는 다음과 같다.
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