def predict(text):
print(f"\nText: {text}")
sentence = Sentence(text)
classifer.predict(sentence)
labels = sentence.labels
print(f"Labels: {labels}")
def read_text_classification_file(path_to_file) -> List[Sentence]:
sentences = []
with open(path_to_file) as f:
lines = f.read().splitlines()
for line in lines:
label_pattern = r"__label__(?P<label>[\w#]+)"
labels = re.findall(label_pattern, line)
labels = [Label(label) for label in labels]
text = re.sub(label_pattern, "", line)
s = Sentence(text, labels)
sentences.append(s)
return sentences
def predict(self, sentence: Sentence):
if self.estimator == TEXT_CLASSIFIER_ESTIMATOR.FAST_TEXT:
values, scores = self.ft.predict(sentence.text)
labels = []
for value, score in zip(values, scores):
value = value.replace("__label__", "")
label = Label(value, score)
labels.append(label)
sentence.add_labels(labels)
if self.estimator == TEXT_CLASSIFIER_ESTIMATOR.SVC:
text = sentence.text
X = self.x_transformer.transform([text])
y = self.svc.predict(X)
y = self.y_transformer.inverse_transform(y)
sentence.add_labels(y)
if self.estimator == TEXT_CLASSIFIER_ESTIMATOR.PIPELINE:
text = sentence.text
y = self.pipeline.predict([text])
if self.multilabel:
y = self.y_encoder.inverse_transform(y)
y = list(y[0])
else:
y = list(y)
sentence.add_labels(y)
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 classify(X):
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.tc_general.value}\".")
sys.exit(1)
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(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_bank.value}\"."
)
sys.exit(1)
sentence = Sentence(text)
classifier.predict(sentence)
labels = sentence.labels
if labels is None:
return None
return [label.value for label in labels]
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]
if label == "1":
label = "negative"
if label == "0":
label = "positive"
return label
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