def classify(texts, output_format, architecture="gru", transformer=None):
# load model
model = Classifier('toxic_' + architecture)
model.load()
start_time = time.time()
result = model.predict(texts, output_format)
print("runtime: %s seconds " % (round(time.time() - start_time, 3)))
return result
def classify(texts, output_format):
# load model
model = Classifier('toxic', "gru", list_classes=list_classes)
model.load()
start_time = time.time()
result = model.predict(texts, output_format)
print("runtime: %s seconds " % (round(time.time() - start_time, 3)))
return result
def test():
# load model
model = Classifier('toxic', "gru", list_classes=list_classes)
model.load()
print('loading test dataset...')
xte = load_texts_pandas("data/textClassification/toxic/test.csv")
print('number of texts to classify:', len(xte))
start_time = time.time()
result = model.predict(xte, output_format="csv")
print("runtime: %s seconds " % (round(time.time() - start_time, 3)))
return result
def classify(texts, output_format):
# load model
model = Classifier('citations', "gru", list_classes=list_classes)
model.load()
start_time = time.time()
result = model.predict(texts, output_format)
runtime = round(time.time() - start_time, 3)
if output_format is 'json':
result["runtime"] = runtime
else:
print("runtime: %s seconds " % (runtime))
return result
def classify(texts, output_format, architecture="gru"):
# load model
model = Classifier('software_use',
model_type=architecture,
list_classes=list_classes)
model.load()
start_time = time.time()
result = model.predict(texts, output_format)
runtime = round(time.time() - start_time, 3)
if output_format is 'json':
result["runtime"] = runtime
else:
print("runtime: %s seconds " % (runtime))
return result
def classify(texts, output_format, architecture="gru", cascaded=False):
'''
Classify a list of texts with an existing model
'''
# load model
model = Classifier('dataseer', model_type=architecture)
model.load()
start_time = time.time()
result = model.predict(texts, output_format)
runtime = round(time.time() - start_time, 3)
if output_format is 'json':
result["runtime"] = runtime
else:
print("runtime: %s seconds " % (runtime))
return result
def classify(texts,
output_format,
embeddings_name=None,
architecture="gru",
transformer=None):
# load model
model = Classifier('software_context_' + architecture)
model.load()
start_time = time.time()
result = model.predict(texts, output_format)
runtime = round(time.time() - start_time, 3)
if output_format == 'json':
result["runtime"] = runtime
else:
print("runtime: %s seconds " % (runtime))
return result