def main():
parser = argparse.ArgumentParser(description='Create Non-projective CRF Dependency Parser.')
parser.set_defaults(Parser=CRFNPDependencyParser)
parser.add_argument('--mbr', action='store_true', help='whether to use MBR decoding')
subparsers = parser.add_subparsers(title='Commands', dest='mode')
subparser = subparsers.add_parser('train', help='Train a parser.')
subparser.add_argument('--feat', '-f', choices=['tag', 'char', 'bert'], help='choices of additional features')
subparser.add_argument('--build', '-b', action='store_true', help='whether to build the model first')
subparser.add_argument('--punct', action='store_true', help='whether to include punctuation')
subparser.add_argument('--max-len', type=int, help='max length of the sentences')
subparser.add_argument('--buckets', default=32, type=int, help='max num of buckets to use')
subparser.add_argument('--train', default='data/ptb/train.conllx', help='path to train file')
subparser.add_argument('--dev', default='data/ptb/dev.conllx', help='path to dev file')
subparser.add_argument('--test', default='data/ptb/test.conllx', help='path to test file')
subparser.add_argument('--embed', default='data/glove.6B.100d.txt', help='path to pretrained embeddings')
subparser.add_argument('--unk', default='unk', help='unk token in pretrained embeddings')
subparser.add_argument('--n-embed', default=100, type=int, help='dimension of embeddings')
subparser.add_argument('--bert', default='bert-base-cased', help='which bert model to use')
# evaluate
subparser = subparsers.add_parser('evaluate', help='Evaluate the specified parser and dataset.')
subparser.add_argument('--punct', action='store_true', help='whether to include punctuation')
subparser.add_argument('--buckets', default=8, type=int, help='max num of buckets to use')
subparser.add_argument('--data', default='data/ptb/test.conllx', help='path to dataset')
# predict
subparser = subparsers.add_parser('predict', help='Use a trained parser to make predictions.')
subparser.add_argument('--prob', action='store_true', help='whether to output probs')
subparser.add_argument('--buckets', default=8, type=int, help='max num of buckets to use')
subparser.add_argument('--data', default='data/ptb/test.conllx', help='path to dataset')
subparser.add_argument('--pred', default='pred.conllx', help='path to predicted result')
parse(parser)
def main():
parser = argparse.ArgumentParser(description='Create Biaffine Semantic Dependency Parser.')
parser.set_defaults(Parser=BiaffineSemanticDependencyParser)
subparsers = parser.add_subparsers(title='Commands', dest='mode')
# train
subparser = subparsers.add_parser('train', help='Train a parser.')
subparser.add_argument('--feat', '-f', choices=['tag', 'char', 'lemma', 'bert'], nargs='+', help='features to use')
subparser.add_argument('--build', '-b', action='store_true', help='whether to build the model first')
subparser.add_argument('--encoder', choices=['lstm', 'bert'], default='lstm', help='encoder to use')
subparser.add_argument('--max-len', type=int, help='max length of the sentences')
subparser.add_argument('--buckets', default=32, type=int, help='max num of buckets to use')
subparser.add_argument('--train', default='data/sdp/DM/train.conllu', help='path to train file')
subparser.add_argument('--dev', default='data/sdp/DM/dev.conllu', help='path to dev file')
subparser.add_argument('--test', default='data/sdp/DM/test.conllu', help='path to test file')
subparser.add_argument('--embed', default='data/glove.6B.100d.txt', help='path to pretrained embeddings')
subparser.add_argument('--unk', default='unk', help='unk token in pretrained embeddings')
subparser.add_argument('--n-embed', default=100, type=int, help='dimension of embeddings')
subparser.add_argument('--n-embed-proj', default=125, type=int, help='dimension of projected embeddings')
subparser.add_argument('--bert', default='bert-base-cased', help='which BERT model to use')
# evaluate
subparser = subparsers.add_parser('evaluate', help='Evaluate the specified parser and dataset.')
subparser.add_argument('--buckets', default=8, type=int, help='max num of buckets to use')
subparser.add_argument('--data', default='data/sdp/DM/test.conllu', help='path to dataset')
# predict
subparser = subparsers.add_parser('predict', help='Use a trained parser to make predictions.')
subparser.add_argument('--buckets', default=8, type=int, help='max num of buckets to use')
subparser.add_argument('--data', default='data/sdp/DM/test.conllu', help='path to dataset')
subparser.add_argument('--pred', default='pred.conllu', help='path to predicted result')
subparser.add_argument('--prob', action='store_true', help='whether to output probs')
parse(parser)
def main():
parser = argparse.ArgumentParser(description='Create Semantic Dependency Parser using Variational Inference.')
parser.set_defaults(Parser=VISemanticDependencyParser)
subparsers = parser.add_subparsers(title='Commands', dest='mode')
# train
subparser = subparsers.add_parser('train', help='Train a parser.')
subparser.add_argument('--feat', '-f', default='tag,char,lemma', help='additional features to use，separated by commas.')
subparser.add_argument('--build', '-b', action='store_true', help='whether to build the model first')
subparser.add_argument('--max-len', type=int, help='max length of the sentences')
subparser.add_argument('--buckets', default=32, type=int, help='max num of buckets to use')
subparser.add_argument('--train', default='data/sdp/DM/train.conllu', help='path to train file')
subparser.add_argument('--dev', default='data/sdp/DM/dev.conllu', help='path to dev file')
subparser.add_argument('--test', default='data/sdp/DM/test.conllu', help='path to test file')
subparser.add_argument('--embed', default='data/glove.6B.100d.txt', help='path to pretrained embeddings')
subparser.add_argument('--unk', default='unk', help='unk token in pretrained embeddings')
subparser.add_argument('--n-embed', default=100, type=int, help='dimension of embeddings')
subparser.add_argument('--bert', default='bert-base-cased', help='which bert model to use')
subparser.add_argument('--inference', default='mfvi', choices=['mfvi', 'lbp'], help='approximate inference methods')
# evaluate
subparser = subparsers.add_parser('evaluate', help='Evaluate the specified parser and dataset.')
subparser.add_argument('--buckets', default=8, type=int, help='max num of buckets to use')
subparser.add_argument('--data', default='data/sdp/DM/test.conllu', help='path to dataset')
# predict
subparser = subparsers.add_parser('predict', help='Use a trained parser to make predictions.')
subparser.add_argument('--prob', action='store_true', help='whether to output probs')
subparser.add_argument('--buckets', default=8, type=int, help='max num of buckets to use')
subparser.add_argument('--data', default='data/sdp/DM/test.conllu', help='path to dataset')
subparser.add_argument('--pred', default='pred.conllu', help='path to predicted result')
parse(parser)
def main():
parser = argparse.ArgumentParser(description='Create Dependency Parser using Variational Inference.')
parser.add_argument('--tree', action='store_true', help='whether to ensure well-formedness')
parser.add_argument('--proj', action='store_true', help='whether to projectivise the data')
parser.add_argument('--partial', action='store_true', help='whether partial annotation is included')
parser.set_defaults(Parser=VIDependencyParser)
subparsers = parser.add_subparsers(title='Commands', dest='mode')
# train
subparser = subparsers.add_parser('train', help='Train a parser.')
subparser.add_argument('--feat', '-f', choices=['tag', 'char', 'bert'], nargs='+', help='features to use')
subparser.add_argument('--build', '-b', action='store_true', help='whether to build the model first')
subparser.add_argument('--encoder', choices=['lstm', 'bert'], default='lstm', help='encoder to use')
subparser.add_argument('--punct', action='store_true', help='whether to include punctuation')
subparser.add_argument('--max-len', type=int, help='max length of the sentences')
subparser.add_argument('--buckets', default=32, type=int, help='max num of buckets to use')
subparser.add_argument('--train', default='data/ptb/train.conllx', help='path to train file')
subparser.add_argument('--dev', default='data/ptb/dev.conllx', help='path to dev file')
subparser.add_argument('--test', default='data/ptb/test.conllx', help='path to test file')
subparser.add_argument('--embed', default='data/glove.6B.100d.txt', help='path to pretrained embeddings')
subparser.add_argument('--unk', default='unk', help='unk token in pretrained embeddings')
subparser.add_argument('--n-embed', default=100, type=int, help='dimension of embeddings')
subparser.add_argument('--bert', default='bert-base-cased', help='which BERT model to use')
subparser.add_argument('--inference', default='mfvi', choices=['mfvi', 'lbp'], help='approximate inference methods')
# evaluate
subparser = subparsers.add_parser('evaluate', help='Evaluate the specified parser and dataset.')
subparser.add_argument('--punct', action='store_true', help='whether to include punctuation')
subparser.add_argument('--buckets', default=8, type=int, help='max num of buckets to use')
subparser.add_argument('--data', default='data/ptb/test.conllx', help='path to dataset')
# predict
subparser = subparsers.add_parser('predict', help='Use a trained parser to make predictions.')
subparser.add_argument('--buckets', default=8, type=int, help='max num of buckets to use')
subparser.add_argument('--data', default='data/ptb/test.conllx', help='path to dataset')
subparser.add_argument('--pred', default='pred.conllx', help='path to predicted result')
subparser.add_argument('--prob', action='store_true', help='whether to output probs')
parse(parser)
