def main(argv=sys.argv):
''' NeuroNER main method
Args:
parameters_filepath the path to the parameters file
output_folder the path to the output folder
'''
arguments = parse_arguments(argv[1:])
# fetch data and models from the package
if arguments['fetch_data'] or arguments['fetch_trained_model']:
if arguments['fetch_data']:
neuromodel.fetch_data(arguments['fetch_data'])
if arguments['fetch_trained_model']:
neuromodel.fetch_model(arguments['fetch_trained_model'])
msg = """When the fetch_data and fetch_trained_model arguments are specified, other
arguments are ignored. Remove these arguments to train or apply a model."""
print(msg)
sys.exit(0)
# create the model
nn = neuromodel.NeuroNER(**arguments)
nn.fit()
nn.close()
def entity_detect(sentence):
# print("Building model")
with HiddenPrints():
neuromodel.fetch_data(dataset)
neuromodel.fetch_model(model)
nn = neuromodel.NeuroNER(train_model=False, use_pretrained_model=True)
# print("predicting")
entities = nn.predict(sentence)
return entities
dataset = 'conll2003'
# 'conll2003'
# 'example_unannotated_texts'
# 'i2b2_2014_deid'
model = 'conll_2003_en'
# 'conll_2003_en'
# 'i2b2_2014_glove_spacy_bioes'
# 'i2b2_2014_glove_stanford_bioes'
# 'mimic_glove_spacy_bioes'
# 'mimic_glove_stanford_bioes'
print("Building model")
with HiddenPrints():
neuromodel.fetch_data(dataset)
neuromodel.fetch_model(model)
nn = neuromodel.NeuroNER(train_model=False, use_pretrained_model=True)
def entity_detect(sentence):
print("predicting")
with HiddenPrints():
entity = nn.predict(sentence)
entities = []
for i in range(len(entity)):
entities.append(entity[i]['text'])
return entities
if __name__ == '__main__':
