def parse_example(parser, sentence):
# load dataset
print('Loading embeddings and ids for parsing')
dataset = load_datasets()
config = dataset.model_config
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
# Make sure the parser is in evaluation mode so it's not using things like dropout
parser.eval()
parse_sentence(sentence, parser, device, dataset)
def test(parser):
# load dataset
print('Loading data for testing')
dataset = load_datasets()
config = dataset.model_config
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# Make sure the parser is in evaluation mode so it's not using things like dropout
parser.eval()
# Compute UAS (unlabeled attachment score), which is the standard evaluate metric for parsers
compute_dependencies(parser, device, dataset.test_data, dataset)
valid_UAS = get_UAS(dataset.test_data)
print("- test UAS: {:.2f}".format(valid_UAS * 100.0))
parser.eval()
test_string = "I shot an elephant with a banana"
parse_sentence(test_string, parser, device, dataset)
def test(parser):
# load dataset
print('Loading data for testing')
dataset = load_datasets()
config = dataset.model_config
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
# Make sure the parser is in evaluation mode so it's not using things like dropout
parser.eval()
# Compute UAS (unlabeled attachment score), which is the standard evaluate metric for parsers.
#
# For details see
# http://www.morganclaypool.com/doi/abs/10.2200/S00169ED1V01Y200901HLT002
# Chapter 6.1
compute_dependencies(parser, device, dataset.test_data, dataset)
valid_UAS = get_UAS(dataset.test_data)
print("- test UAS: {:.2f}".format(valid_UAS * 100.0))
parser.eval()
test_string = "I shot an elephant with a banana"
parse_sentence(test_string, parser, device, dataset)
