class DNDMVModelRunner(Runner):
def __init__(self, o: DNMDVModelOptions):
if o.use_softmax_em:
from utils import common
common.cpf = cp.float64
m = DNDMVModel(o, self)
super().__init__(m, o, Logger(o))
def load(self):
pos_vocab_list = [w.strip() for w in open(self.o.vocab_path)]
pos_vocab = Vocab.from_list(pos_vocab_list)
self.train_ds = ConllDataset(self.o.train_ds, pos_vocab=pos_vocab)
self.dev_ds = ConllDataset(self.o.dev_ds, pos_vocab=pos_vocab)
self.test_ds = ConllDataset(self.o.test_ds, pos_vocab=pos_vocab)
if self.o.pretrained_ds:
self.pretrained_ds = ConllDataset(self.o.pretrained_ds, pos_vocab=pos_vocab)
else:
self.pretrained_ds = None
self.dev_ds.build_batchs(self.o.batch_size)
self.test_ds.build_batchs(self.o.batch_size)
if self.o.emb_path:
self.word_emb = np.load(self.o.emb_path)
else:
self.word_emb = None
def load(self):
self.train_ds = ConllDataset(self.o.train_ds,
pos_vocab=WSJ_POS,
sort=True)
self.dev_ds = ConllDataset(self.o.dev_ds, pos_vocab=WSJ_POS)
self.test_ds = ConllDataset(self.o.test_ds, pos_vocab=WSJ_POS)
self.dev_ds.build_batchs(self.o.batch_size)
self.test_ds.build_batchs(self.o.batch_size)
def load(self):
self.train_ds = ConllDataset(self.o.train_ds, pos_vocab=WSJ_POS)
self.dev_ds = ConllDataset(self.o.dev_ds, pos_vocab=WSJ_POS)
self.test_ds = ConllDataset(self.o.test_ds, pos_vocab=WSJ_POS)
self.dev_ds.build_batchs(10000)
self.test_ds.build_batchs(10000)
self.o.max_len = 10
self.o.num_tag = len(WSJ_POS)
class DMVModelRunner(Runner):
def __init__(self, o: DMVModelOptions):
m = DMVModel(o, self)
super().__init__(m, o, Logger())
print(self.workspace)
def load(self):
self.train_ds = ConllDataset(self.o.train_ds, pos_vocab=WSJ_POS)
self.dev_ds = ConllDataset(self.o.dev_ds, pos_vocab=WSJ_POS)
self.test_ds = ConllDataset(self.o.test_ds, pos_vocab=WSJ_POS)
self.dev_ds.build_batchs(10000)
self.test_ds.build_batchs(10000)
self.o.max_len = 10
self.o.num_tag = len(WSJ_POS)
class NDMVModelRunner(Runner):
def __init__(self, o):
if o.use_softmax_em:
from utils import common
common.cpf = cp.float64
m = NDMVModel(o, self)
super().__init__(m, o, Logger(o))
def load(self):
self.train_ds = ConllDataset(self.o.train_ds,
pos_vocab=WSJ_POS,
sort=True)
self.dev_ds = ConllDataset(self.o.dev_ds, pos_vocab=WSJ_POS)
self.test_ds = ConllDataset(self.o.test_ds, pos_vocab=WSJ_POS)
self.dev_ds.build_batchs(self.o.batch_size)
self.test_ds.build_batchs(self.o.batch_size)
def load(self):
if self.o.use_pair:
wordpos_vocab_list = [
w.strip().split() for w in open(self.o.vocab_path)
][:self.o.num_lex + 2]
word_vocab_list = [wp[0] for wp in wordpos_vocab_list]
word_vocab = Vocab.from_list(word_vocab_list,
unk='<UNK>',
pad='<PAD>')
self.pos_dict = {
word_vocab[wp[0]]: list(map(BLLIP_POS.__getitem__, wp[1:]))
for wp in wordpos_vocab_list if len(wp) > 1
}
else:
word_vocab_list = [w.strip() for w in open(self.o.vocab_path)
][:self.o.num_lex + 2]
word_vocab = Vocab.from_list(word_vocab_list,
unk='<UNK>',
pad='<PAD>')
self.train_ds = ConllDataset(self.o.train_ds,
pos_vocab=BLLIP_POS,
word_vocab=word_vocab)
self.dev_ds = ConllDataset(self.o.dev_ds,
pos_vocab=BLLIP_POS,
word_vocab=word_vocab)
self.test_ds = ConllDataset(self.o.test_ds,
pos_vocab=BLLIP_POS,
word_vocab=word_vocab)
if self.o.pretrained_ds:
self.pretrained_ds = ConllDataset(self.o.pretrained_ds,
pos_vocab=BLLIP_POS,
word_vocab=word_vocab)
else:
self.pretrained_ds = None
self.dev_ds.build_batchs(self.o.batch_size)
self.test_ds.build_batchs(self.o.batch_size)
if self.o.use_pair:
self.o.num_lex = sum([len(p) for p in self.pos_dict.values()])
self.o.max_len = 10
self.o.num_tag = len(BLLIP_POS) + self.o.num_lex
if self.o.emb_path:
self.word_emb = np.load(self.o.emb_path)[:self.o.num_lex + 2]
else:
self.word_emb = None
self.out_pos_emb = np.load(
self.o.out_pos_emb_path) if self.o.out_pos_emb_path else None
self.pos_emb = np.load(
self.o.pos_emb_path) if self.o.pos_emb_path else None
def load(self):
pos_vocab_list = [w.strip() for w in open(self.o.vocab_path)]
pos_vocab = Vocab.from_list(pos_vocab_list)
self.train_ds = ConllDataset(self.o.train_ds, pos_vocab=pos_vocab)
self.dev_ds = ConllDataset(self.o.dev_ds, pos_vocab=pos_vocab)
self.test_ds = ConllDataset(self.o.test_ds, pos_vocab=pos_vocab)
if self.o.pretrained_ds:
self.pretrained_ds = ConllDataset(self.o.pretrained_ds, pos_vocab=pos_vocab)
else:
self.pretrained_ds = None
self.dev_ds.build_batchs(self.o.batch_size)
self.test_ds.build_batchs(self.o.batch_size)
if self.o.emb_path:
self.word_emb = np.load(self.o.emb_path)
else:
self.word_emb = None
class LNDMVModelRunner(Runner):
def __init__(self, o: LNMDVModelOptions):
if o.use_softmax_em:
from utils import common
common.cpf = cp.float64
m = LNDMVModel(o, self)
super().__init__(m, o, Logger(o))
def load(self):
if self.o.use_pair:
wordpos_vocab_list = [
w.strip().split() for w in open(self.o.vocab_path)
][:self.o.num_lex + 2]
word_vocab_list = [wp[0] for wp in wordpos_vocab_list]
word_vocab = Vocab.from_list(word_vocab_list,
unk='<UNK>',
pad='<PAD>')
self.pos_dict = {
word_vocab[wp[0]]: list(map(BLLIP_POS.__getitem__, wp[1:]))
for wp in wordpos_vocab_list if len(wp) > 1
}
else:
word_vocab_list = [w.strip() for w in open(self.o.vocab_path)
][:self.o.num_lex + 2]
word_vocab = Vocab.from_list(word_vocab_list,
unk='<UNK>',
pad='<PAD>')
self.train_ds = ConllDataset(self.o.train_ds,
pos_vocab=BLLIP_POS,
word_vocab=word_vocab)
self.dev_ds = ConllDataset(self.o.dev_ds,
pos_vocab=BLLIP_POS,
word_vocab=word_vocab)
self.test_ds = ConllDataset(self.o.test_ds,
pos_vocab=BLLIP_POS,
word_vocab=word_vocab)
if self.o.pretrained_ds:
self.pretrained_ds = ConllDataset(self.o.pretrained_ds,
pos_vocab=BLLIP_POS,
word_vocab=word_vocab)
else:
self.pretrained_ds = None
self.dev_ds.build_batchs(self.o.batch_size)
self.test_ds.build_batchs(self.o.batch_size)
if self.o.use_pair:
self.o.num_lex = sum([len(p) for p in self.pos_dict.values()])
self.o.max_len = 10
self.o.num_tag = len(BLLIP_POS) + self.o.num_lex
if self.o.emb_path:
self.word_emb = np.load(self.o.emb_path)[:self.o.num_lex + 2]
else:
self.word_emb = None
self.out_pos_emb = np.load(
self.o.out_pos_emb_path) if self.o.out_pos_emb_path else None
self.pos_emb = np.load(
self.o.pos_emb_path) if self.o.pos_emb_path else None