def __init__(self,
embed_model_path=default_embed_model_path,
cls_model_path=default_cls_model_path):
if not os.path.isfile(embed_model_path):
print('embed_model doesn\'t exist.\ncheck the model path')
if not os.path.isfile(cls_model_path):
print('classifier_model doesn\'t exist.\ncheck the model path')
# 전처리 패키지 초기화
print('Init preprocessing')
initialize(KMR='2.1.4') #LATEST--> 2.1.4로 변경
self.tagger = Tagger(API.KMR)
# 임베딩 모델 로딩
print('Loading Embedding model')
self.embedding_model = Doc2Vec.load(embed_model_path)
# Classifier 모델 로딩
print('Loading Classifier model')
with open(cls_model_path, 'rb') as fp:
self.clf_model = pickle.load(fp)
def init_koala_nlp():
initialize(HNN="LATEST")
from koalanlp.Util import initialize, finalize
from koalanlp.proc import Parser
from koalanlp import API
initialize(KKMA='LATEST') #: HNN=2.0.4, ETRI=2.0.4
parser = Parser(API.KKMA)
while True:
text = input("분석할 문장을 입력하세요>> ").strip()
if len(text) == 0:
break
sentences = parser(text)
for sent in sentences:
print("===== Sentence =====")
print(sent.singleLineString())
print("# Dependency Parse result")
dependencies = sent.getDependencies()
if len(dependencies) > 0:
for edge in dependencies:
print("[%s]는 [%s]의 %s-%s" %
(edge.getDependent().getSurface(),
edge.getGovernor().getSurface() if edge.getGovernor()
is not None else "ROOT", str(
edge.getType()), str(edge.getDepType())))
else:
print("(Unexpected) NULL!")
import torch
import torch.nn.functional as F
from gluonnlp.data import SentencepieceTokenizer
from kogpt2.utils import get_tokenizer
from kogpt2.utils import download, tokenizer
from kogpt2.model.torch_gpt2 import GPT2LMHeadModel
from kogpt2.configuration_gpt2 import GPT2Config
import gluonnlp
from koalanlp.Util import initialize, finalize
from koalanlp.proc import Tagger
from koalanlp import API
from collections import Counter
initialize(EUNJEON="LATEST")
tagger = Tagger(API.EUNJEON)
pytorch_kogpt2 = {
"url": "./checkpoint/pytorch_kogpt2_676e9bcfa7.params",
"fname": "pytorch_kogpt2_676e9bcfa7.params",
"chksum": "676e9bcfa7",
}
kogpt2_config = {
"initializer_range": 0.02,
"layer_norm_epsilon": 1e-05,
"n_ctx": 1024,
"n_embd": 768,
"n_head": 12,
"n_layer": 12,
"n_positions": 1024,
"vocab_size": 50000,
