def __init__(self, sentences=None, size=300, alpha=0.025, window=8, min_count=5,
sample=0, seed=1, workers=1, min_alpha=0.0001, dm=1, hs=1, negative=0,
dm_mean=0, train_words=True, train_lbls=True, **kwargs):
"""
Initialize the model from an iterable of `sentences`. Each sentence is a
LabeledSentence object that will be used for training.
The `sentences` iterable can be simply a list of LabeledSentence elements, but for larger corpora,
consider an iterable that streams the sentences directly from disk/network.
If you don't supply `sentences`, the model is left uninitialized -- use if
you plan to initialize it in some other way.
`dm` defines the training algorithm. By default (`dm=1`), distributed memory is used.
Otherwise, `dbow` is employed.
`size` is the dimensionality of the feature vectors.
`window` is the maximum distance between the current and predicted word within a sentence.
`alpha` is the initial learning rate (will linearly drop to zero as training progresses).
`seed` = for the random number generator.
`min_count` = ignore all words with total frequency lower than this.
`sample` = threshold for configuring which higher-frequency words are randomly downsampled;
default is 0 (off), useful value is 1e-5.
`workers` = use this many worker threads to train the model (=faster training with multicore machines).
`hs` = if 1 (default), hierarchical sampling will be used for model training (else set to 0).
`negative` = if > 0, negative sampling will be used, the int for negative
specifies how many "noise words" should be drawn (usually between 5-20).
`dm_mean` = if 0 (default), use the sum of the context word vectors. If 1, use the mean.
Only applies when dm is used.
"""
Word2Vec.__init__(self, size=size, alpha=alpha, window=window, min_count=min_count,
sample=sample, seed=seed, workers=workers, min_alpha=min_alpha,
sg=(1+dm) % 2, hs=hs, negative=negative, cbow_mean=dm_mean, **kwargs)
self.train_words = train_words
self.train_lbls = train_lbls
if sentences is not None:
self.build_vocab(sentences)
self.train(sentences)
def __init__(self, sentences=None, size=300, alpha=0.025, window=8, min_count=5,
sample=0, seed=1, workers=1, min_alpha=0.0001, dm=1, hs=1, negative=0,
dm_mean=0, train_words=True, train_lbls=True, **kwargs):
"""
Initialize the model from an iterable of `sentences`. Each sentence is a
LabeledSentence object that will be used for training.
The `sentences` iterable can be simply a list of LabeledSentence elements, but for larger corpora,
consider an iterable that streams the sentences directly from disk/network.
If you don't supply `sentences`, the model is left uninitialized -- use if
you plan to initialize it in some other way.
`dm` defines the training algorithm. By default (`dm=1`), distributed memory is used.
Otherwise, `dbow` is employed.
`size` is the dimensionality of the feature vectors.
`window` is the maximum distance between the current and predicted word within a sentence.
`alpha` is the initial learning rate (will linearly drop to zero as training progresses).
`seed` = for the random number generator.
`min_count` = ignore all words with total frequency lower than this.
`sample` = threshold for configuring which higher-frequency words are randomly downsampled;
default is 0 (off), useful value is 1e-5.
`workers` = use this many worker threads to train the model (=faster training with multicore machines).
`hs` = if 1 (default), hierarchical sampling will be used for model training (else set to 0).
`negative` = if > 0, negative sampling will be used, the int for negative
specifies how many "noise words" should be drawn (usually between 5-20).
`dm_mean` = if 0 (default), use the sum of the context word vectors. If 1, use the mean.
Only applies when dm is used.
"""
Word2Vec.__init__(self, size=size, alpha=alpha, window=window, min_count=min_count,
sample=sample, seed=seed, workers=workers, min_alpha=min_alpha,
sg=(1+dm) % 2, hs=hs, negative=negative, cbow_mean=dm_mean, **kwargs)
self.train_words = train_words
self.train_lbls = train_lbls
if sentences is not None:
self.build_vocab(sentences)
self.train(sentences)
def __init__(self, size=50, alpha=0.1, min_count=1, seed=1, workers=1, iter=1, use_gold=0, train_path=None,
test_raw_path=None, test_path=None, dev_path=None, quick_test=None, dict_path=None,
score_script_path=None, pre_train=False, uni_path=None, bi_path=None, hybrid_pred=False,
no_action_feature=False, no_bigram_feature=False, no_unigram_feature=False,
no_binary_action_feature=False, no_sb_state_feature=False, **kwargs):
print '\n\n### Initialization of the segmentation model ###'
self.no_action_feature = no_action_feature
self.no_bigram_feature = no_bigram_feature
self.no_unigram_feature = no_unigram_feature
self.no_binary_action_feature = no_binary_action_feature
self.no_sb_feature = no_sb_state_feature
self.pre_train = pre_train
self.l2_rate = 0.001 # rate for L2 regularization
if self.l2_rate:
print 'reg with L2, with param=', self.l2_rate
self.drop_out = False
self.finger_int = str(r_randint(0, 1000000))
self.binary_pred = False
self.hybrid_pred = hybrid_pred
self.use_gold = use_gold
# self.model = None
self.START = "#S#"
self.END = "#E#"
self.label0_as_vocab, self.label1_as_vocab, self.unknown_as_vocab = "$LABEL0", "$LABEL1", "$OOV"
self.su_prefix, self.sb_prefix = '$SU', '$SB' # prefix for unigram/bigram state; no prefix for *char* unigram/bigrams
self.state_varient = ('0', '1')
self.train_path = train_path
self.test_raw_path = test_raw_path
self.test_path = test_path
self.dev_path = dev_path
self.quick_test = quick_test
self.dict_path = dict_path
self.score_script = score_script_path
# self.score_script = '../working_data/score'
# self.dict_path ='../working_data/pku.dict'
print '\nloading train, test, dev corpus...'
self.train_corpus = [l.split() for l in codecs.open(self.train_path, 'rU', 'utf-8')]
self.test_corpus = [l.split() for l in codecs.open(self.test_raw_path, 'rU', 'utf-8')]
self.dev_corpus = [l.split() for l in codecs.open(self.dev_path, 'rU', 'utf-8')]
self.quick_test_corpus = [l.split() for l in codecs.open(self.quick_test, 'rU', 'utf-8')]
Word2Vec.__init__(self, sentences=None, size=size, alpha=alpha, min_count=min_count, seed=seed, workers=workers,
iter=iter, **kwargs)
self.mask = [1 for i in range(12)]
if self.no_action_feature:
self.mask = self.mask[:-3]
print 'len mask', len(self.mask)
elif self.no_sb_feature:
self.mask = self.mask[:-1]
print 'len mask', len(self.mask)
if self.no_unigram_feature:
self.mask = self.mask[:-5]
print 'len mask', len(self.mask)
if self.no_bigram_feature:
self.mask = self.mask[:-4]
print 'len mask', len(self.mask)
self.f_factor = sum(self.mask)
self.f_factor2 = 2
if self.no_binary_action_feature:
self.f_factor2 = 0
print 'f-factor2=', self.f_factor2
self.non_fixed_param = self.f_factor * self.layer1_size
self.pred_size = self.non_fixed_param + self.f_factor2
if self.drop_out:
self.dropout_rate = 0.5
self.dropout_size = int(self.dropout_rate * self.non_fixed_param)
print 'using drop_out, rate/size=', self.dropout_rate, self.dropout_size
self.train_mode = False
self.dev_test_result = []
print '\nLearning rate=', self.alpha, '; Feature (layer1) size=', self.layer1_size, '; Predicate vec size=', self.pred_size, 'f-factor=', self.f_factor, 'f-factor2=', self.f_factor2
if self.pre_train:
print '\nloading pre-trained char and char-bigram embeddings'
self.uni_emb = Word2Vec.load(uni_path)
emb_normalization(self.uni_emb)
print 'unigram embedding loaded'
self.bi_emb = Word2Vec.load(bi_path)
emb_normalization(self.bi_emb)
print 'bigram embedding loaded'
def __init__(self,
size=50,
alpha=0.1,
min_count=1,
seed=1,
workers=1,
iter=1,
use_gold=0,
train_path=None,
test_raw_path=None,
test_path=None,
dev_path=None,
quick_test=None,
dict_path=None,
score_script_path=None,
pre_train=False,
uni_path=None,
bi_path=None,
hybrid_pred=False,
no_action_feature=False,
no_bigram_feature=False,
no_unigram_feature=False,
no_binary_action_feature=False,
no_sb_state_feature=False,
**kwargs):
print '\n\n### Initialization of the segmentation model ###'
self.no_action_feature = no_action_feature
self.no_bigram_feature = no_bigram_feature
self.no_unigram_feature = no_unigram_feature
self.no_binary_action_feature = no_binary_action_feature
self.no_sb_feature = no_sb_state_feature
self.pre_train = pre_train
self.l2_rate = 0.001 # rate for L2 regularization
if self.l2_rate:
print 'reg with L2, with param=', self.l2_rate
self.drop_out = False
self.finger_int = str(r_randint(0, 1000000))
self.binary_pred = False
self.hybrid_pred = hybrid_pred
self.use_gold = use_gold
# self.model = None
self.START = "#S#"
self.END = "#E#"
self.label0_as_vocab, self.label1_as_vocab, self.unknown_as_vocab = "$LABEL0", "$LABEL1", "$OOV"
self.su_prefix, self.sb_prefix = '$SU', '$SB' # prefix for unigram/bigram state; no prefix for *char* unigram/bigrams
self.state_varient = ('0', '1')
self.train_path = train_path
self.test_raw_path = test_raw_path
self.test_path = test_path
self.dev_path = dev_path
self.quick_test = quick_test
self.dict_path = dict_path
self.score_script = score_script_path
# self.score_script = '../working_data/score'
# self.dict_path ='../working_data/pku.dict'
print '\nloading train, test, dev corpus...'
self.train_corpus = [
l.split() for l in codecs.open(self.train_path, 'rU', 'utf-8')
]
self.test_corpus = [
l.split() for l in codecs.open(self.test_raw_path, 'rU', 'utf-8')
]
self.dev_corpus = [
l.split() for l in codecs.open(self.dev_path, 'rU', 'utf-8')
]
self.quick_test_corpus = [
l.split() for l in codecs.open(self.quick_test, 'rU', 'utf-8')
]
Word2Vec.__init__(self,
sentences=None,
size=size,
alpha=alpha,
min_count=min_count,
seed=seed,
workers=workers,
iter=iter,
**kwargs)
self.mask = [1 for i in range(12)]
if self.no_action_feature:
self.mask = self.mask[:-3]
print 'len mask', len(self.mask)
elif self.no_sb_feature:
self.mask = self.mask[:-1]
print 'len mask', len(self.mask)
if self.no_unigram_feature:
self.mask = self.mask[:-5]
print 'len mask', len(self.mask)
if self.no_bigram_feature:
self.mask = self.mask[:-4]
print 'len mask', len(self.mask)
self.f_factor = sum(self.mask)
self.f_factor2 = 2
if self.no_binary_action_feature:
self.f_factor2 = 0
print 'f-factor2=', self.f_factor2
self.non_fixed_param = self.f_factor * self.layer1_size
self.pred_size = self.non_fixed_param + self.f_factor2
if self.drop_out:
self.dropout_rate = 0.5
self.dropout_size = int(self.dropout_rate * self.non_fixed_param)
print 'using drop_out, rate/size=', self.dropout_rate, self.dropout_size
self.train_mode = False
self.dev_test_result = []
print '\nLearning rate=', self.alpha, '; Feature (layer1) size=', self.layer1_size, '; Predicate vec size=', self.pred_size, 'f-factor=', self.f_factor, 'f-factor2=', self.f_factor2
if self.pre_train:
print '\nloading pre-trained char and char-bigram embeddings'
self.uni_emb = Word2Vec.load(uni_path)
emb_normalization(self.uni_emb)
print 'unigram embedding loaded'
self.bi_emb = Word2Vec.load(bi_path)
emb_normalization(self.bi_emb)
print 'bigram embedding loaded'
