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(pretrain, model_file)
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 = Parser(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'],
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):
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'])
print("[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,
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_from_lm(self,
lm_model,
freeze: bool = True,
m_names=[
'word_emb', 'lemma_emb', 'upos_emb', 'xpos_emb',
'ufeats_emb', 'charmodel', 'trans_char', 'trans_char',
'trans_pretrained', 'lstm_forward', 'lstm_backward'
]):
"""
Initialize the paramters from a pretrained langauge model.
lm_model: LSTMBiLM object
freeze: bool (optional)
if True, the initialized paramters are freezed and will be from the optimizer's param group
m_names: List[str] (optional)
a list of module names to initialize
"""
for m in m_names:
if hasattr(self.model, m):
print('Initilizing {}'.format(m))
if not hasattr(lm_model, m):
raise ValueError(
'pretrained language model does not have attribute {}'.
format(m))
module = getattr(self.model, m)
copy_weights(getattr(lm_model, m), module)
if freeze:
freeze_net(module)
else:
print('Skipping {}'.format(m))
self.parameters = [
p for p in self.model.parameters() if p.requires_grad
]
self.optimizer = utils.get_optimizer(self.args['optim'],
self.parameters,
self.args['lr'],
betas=(self.args['beta1'],
self.args['beta2']),
weight_decay=self.args['wdecay'])
