def __init__(self,
args=None,
vocab=None,
emb_matrix=None,
model_file=None,
use_cuda=False):
self.use_cuda = use_cuda
if model_file is not None:
# load from file
self.load(model_file, use_cuda)
else:
self.args = args
self.model = None if args['dict_only'] else Seq2SeqModel(
args, emb_matrix=emb_matrix)
self.vocab = vocab
self.expansion_dict = dict()
if not self.args['dict_only']:
self.crit = loss.SequenceLoss(self.vocab.size)
self.parameters = [
p for p in self.model.parameters() if p.requires_grad
]
if use_cuda:
self.model.cuda()
self.crit.cuda()
else:
self.model.cpu()
self.crit.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'],
self.parameters,
self.args['lr'])
def __init__(self,
args=None,
vocab=None,
pretrain=None,
model_file=None,
use_cuda=False):
self.use_cuda = use_cuda
if model_file is not None:
# load everything from file
self.load(model_file, pretrain)
else:
# build model from scratch
self.args = args
self.vocab = vocab
self.model = Parser(
args,
vocab,
emb_matrix=pretrain.emb if pretrain is not None else None)
self.parameters = [
p for p in self.model.parameters() if p.requires_grad
]
if self.use_cuda:
self.model.cuda()
else:
self.model.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'],
self.parameters,
self.args['lr'],
betas=(0.9, self.args['beta2']),
eps=1e-6)
def __init__(self, args=None, vocab=None, emb_matrix=None, model_file=None, use_cuda=False, teacher_model_file=None):
self.use_cuda = use_cuda
self.vocab = vocab
if model_file is not None:
# load everything from file
self.load(model_file, use_cuda, args=args, vocab=vocab)
else:
# build model from scratch
self.args = args
self.model = Seq2SeqModel(args, emb_matrix=emb_matrix, use_cuda=use_cuda)
# dict-based components
self.word_dict = dict()
self.composite_dict = dict()
self.crit = torch.nn.NLLLoss(ignore_index=constant.PAD_ID)
self.nce_parameters = []
if self.args['lambda2'] != 0:
self.init_teacher(teacher_model_file, use_cuda, emb_matrix=emb_matrix)
self.teacher_update_count = 0
self.parameters = [p for p in self.model.parameters() if p.requires_grad]
if use_cuda:
self.model.cuda()
self.crit.cuda()
else:
self.model.cpu()
self.crit.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'], self.parameters, self.args['lr'])
def __init__(self,
args=None,
vocab=None,
pretrain=None,
model_file=None,
use_cuda=False,
train_classifier_only=False):
self.use_cuda = use_cuda
if model_file is not None:
# load everything from file
self.load(model_file, args)
else:
assert all(var is not None for var in [args, vocab, pretrain])
# build model from scratch
self.args = args
self.vocab = vocab
self.model = NERTagger(args, vocab, emb_matrix=pretrain.emb)
if train_classifier_only:
logger.info('Disabling gradient for non-classifier layers')
exclude = ['tag_clf', 'crit']
for pname, p in self.model.named_parameters():
if pname.split('.')[0] not in exclude:
p.requires_grad = False
self.parameters = [
p for p in self.model.parameters() if p.requires_grad
]
if self.use_cuda:
self.model.cuda()
else:
self.model.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'],
self.parameters,
self.args['lr'],
momentum=self.args['momentum'])
def __init__(self, args=None, vocab=None, emb_matrix=None, model_file=None, use_cuda=False):
self.use_cuda = use_cuda
if model_file is not None:
# load everything from file
self.load(model_file, use_cuda)
else:
# build model from scratch
self.args = args
self.model = None if args['dict_only'] else Seq2SeqModel(args, emb_matrix=emb_matrix, use_cuda=use_cuda)
self.vocab = vocab
# dict-based components
self.word_dict = dict()
self.composite_dict = dict()
if not self.args['dict_only']:
if self.args.get('edit', False):
self.crit = loss.MixLoss(self.vocab['char'].size, self.args['alpha'])
logger.debug("Running seq2seq lemmatizer with edit classifier...")
else:
self.crit = loss.SequenceLoss(self.vocab['char'].size)
self.parameters = [p for p in self.model.parameters() if p.requires_grad]
if use_cuda:
self.model.cuda()
self.crit.cuda()
else:
self.model.cpu()
self.crit.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'], self.parameters, self.args['lr'])
def __init__(self,
args=None,
vocab=None,
pretrain=None,
model_file=None,
use_cuda=False):
self.use_cuda = use_cuda
if model_file is not None:
# load everything from file
self.load(model_file, args)
else:
assert all(var is not None for var in [args, vocab, pretrain])
# build model from scratch
self.args = args
self.vocab = vocab
self.model = NERTagger(args, vocab, emb_matrix=pretrain.emb)
self.parameters = [
p for p in self.model.parameters() if p.requires_grad
]
if self.use_cuda:
self.model.cuda()
else:
self.model.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'],
self.parameters,
self.args['lr'],
momentum=self.args['momentum'])
