def classify(X):
global classifier
model_name = 'TC_GENERAL_V131'
model_path = ModelFetcher.get_model_path(model_name)
if not classifier:
if not os.path.exists(model_path):
ModelFetcher.download(model_name)
classifier = TextClassifier.load(model_path)
sentence = Sentence(X)
classifier.predict(sentence)
labels = sentence.labels
return labels
def sentiment(X):
global classifier
model_name = 'SA_BANK_V131'
model_path = ModelFetcher.get_model_path(model_name)
if not classifier:
if not os.path.exists(model_path):
ModelFetcher.download(model_name)
classifier = TextClassifier.load(model_path)
sentence = Sentence(X)
classifier.predict(sentence)
labels = sentence.labels
if not labels:
return None
labels = [label.value for label in labels]
return labels
def sentiment(X):
global classifier
model_name = 'SA_GENERAL_V131'
model_path = ModelFetcher.get_model_path(model_name)
if not classifier:
if not os.path.exists(model_path):
ModelFetcher.download(model_name)
classifier = TextClassifier.load(model_path)
sentence = Sentence(X)
classifier.predict(sentence)
labels = sentence.labels
try:
label_map = {'POS': 'positive', 'NEG': 'negative'}
label = label_map[labels[0]]
return label
except Exception:
return None
import logging
import os
import sys
from os.path import dirname
from underthesea.corpus.data import Sentence
from underthesea.models.text_classifier import TextClassifier
from underthesea.model_fetcher import ModelFetcher, UTSModel
FORMAT = '%(message)s'
logging.basicConfig(format=FORMAT)
logger = logging.getLogger('underthesea')
sys.path.insert(0, dirname(dirname(__file__)))
model_path = ModelFetcher.get_model_path(UTSModel.tc_bank)
classifier = None
sys.path.insert(0, dirname(dirname(__file__)))
classifier = None
def sentiment(X):
global classifier
model_name = 'SA_BANK_V131'
model_path = ModelFetcher.get_model_path(model_name)
if not classifier:
if not os.path.exists(model_path):
ModelFetcher.download(model_name)
classifier = TextClassifier.load(model_path)
sentence = Sentence(X)
def remove_model(model):
ModelFetcher.remove(model)
def download_model(model):
ModelFetcher.download(model)
def list_model(all):
ModelFetcher.list(all)
import logging
import os
import sys
from languageflow.data import Sentence
from languageflow.models.text_classifier import TextClassifier
from underthesea.model_fetcher import ModelFetcher, UTSModel
from . import text_features
FORMAT = '%(message)s'
logging.basicConfig(format=FORMAT)
logger = logging.getLogger('underthesea')
sys.modules['text_features'] = text_features
model_path = ModelFetcher.get_model_path(UTSModel.sa_general)
classifier = None
def sentiment(text):
global classifier
if not classifier:
if os.path.exists(model_path):
classifier = TextClassifier.load(model_path)
else:
logger.error(
f"Could not load model at {model_path}.\n"
f"Download model with \"underthesea download {UTSModel.sa_general.value}\"."
)
sys.exit(1)
sentence = Sentence(text)
classifier.predict(sentence)
label = sentence.labels[0]