def Base(eta, l2, tagversion, hidden, batchsize):
params.outfile = 'ccctag_CRF_Bilstm_Viterbi_'
params.dataf = '../supertag_data/train.dat'
params.dev = '../supertag_data/dev.dat'
params.test = '../supertag_data/test.dat'
params.batchsize = batchsize
params.hidden = hidden
params.embedsize = 100
params.emb = 0
params.eta = eta
params.L2 = l2
params.dropout = 0
params.tagversion = tagversion
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
if (tagversion == 0):
tagger = getTagger('../supertag_data/tagger_100')
elif (tagversion == 1):
tagger = getTagger('../supertag_data/tagger_200')
elif (tagversion == 2):
tagger = getTagger('../supertag_data/tagger_400')
else:
tagger = getTagger('../supertag_data/tagger')
params.num_labels = len(tagger)
print len(tagger)
params.outfile = params.outfile + ".Batchsize" + '_' + str(
params.batchsize) + '_dropout_' + str(
params.dropout) + "_LearningRate" + '_' + str(
params.eta) + '_' + str(l2) + str(
hidden) + '_tagversoin_' + str(tagversion)
#examples are shuffled data
traindata = getSupertagData(params.dataf, words, tagger)
trainx0, trainy0 = traindata
devdata = getSupertagData(params.dev, words, tagger, train=False)
devx0, devy0 = devdata
print 'dev set', len(devx0)
testdata = getSupertagData(params.test, words, tagger, train=False)
testx0, testy0 = testdata
print 'test set', len(testx0)
#print Y
print "Using Training Data" + params.dataf
print "Using Word Embeddings with Dimension " + str(params.embedsize)
print "Saving models to: " + params.outfile
#lm = LM_model(params)
#lm.train(trainy0, devy0, params)
tm = CRF_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
def Base(eta, l2, morepara, emb, batchsize):
params.outfile = 'NER_CRF_lstm_Viterti_'
params.dataf = '../ner_data/eng.train.bioes.conll'
params.dev = '../ner_data/eng.dev.bioes.conll'
params.test = '../ner_data/eng.test.bioes.conll'
params.batchsize = batchsize
params.hidden = 100
params.embedsize = 100
params.emb = emb
params.eta = eta
params.L2 = l2
params.dropout = 0
params.num_labels = 17
params.morepara = morepara
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
tagger = getTagger('../ner_data/ner_bioes')
print tagger
params.taggerlist = getTaggerlist('../ner_data/ner_bioes')
params.outfile = params.outfile + ".Batchsize" + '_' + str(
params.batchsize
) + '_dropout_' + str(params.dropout) + "_LearningRate" + '_' + str(
params.eta) + '_' + str(l2) + str(morepara) + '_emb_' + str(emb)
#examples are shuffled data
trainx0, trainy0, _, _ = Get_Ner_bioes(params.dataf, words, tagger)
traindata = trainx0, trainy0
#N = int(params.frac*len(trainx0))
#traindata = trainx0[:N], trainy0[:N]
devx0, devy0, params.devrawx, params.devpos = Get_Ner_bioes(
params.dev, words, tagger)
devdata = devx0, devy0
print devy0[:10]
print 'dev set', len(devx0)
testx0, testy0, params.testrawx, params.testpos = Get_Ner_bioes(
params.test, words, tagger)
testdata = testx0, testy0
print 'test set', len(testx0)
#print Y
print "Using Training Data" + params.dataf
print "Using Word Embeddings with Dimension " + str(params.embedsize)
print "Saving models to: " + params.outfile
tm = CRF_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
def Base(eta, l2, num_filters, emb, hidden):
params.outfile = 'NER_BiLSTM_CNN_CRF_'
params.dataf = '../ner_data/eng.train.bioes.conll'
params.dev = '../ner_data/eng.dev.bioes.conll'
params.test = '../ner_data/eng.test.bioes.conll'
params.batchsize = 10
params.hidden = hidden
params.embedsize = 100
params.emb = emb
params.eta = eta
params.L2 = l2
params.dropout = 1
params.num_labels = 17
params.char_embedd_dim = 30
params.num_filters = num_filters
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
tagger = getTagger('../ner_data/ner_bioes')
print tagger
char_dic = getTagger('../ner_data/char_dic')
params.char_dic = char_dic
scale = np.sqrt(3.0 / params.char_embedd_dim)
char_embedd_table = np.random.uniform(
-scale, scale,
[len(char_dic), params.char_embedd_dim]).astype(theano.config.floatX)
params.taggerlist = getTaggerlist('../ner_data/ner_bioes')
params.outfile = params.outfile + ".Batchsize" + '_' + str(
params.batchsize) + '_dropout_' + str(
params.dropout) + "_LearningRate" + '_' + str(
params.eta) + '_' + str(l2) + '_' + str(
num_filters) + '_hidden_' + str(hidden)
trainx0, trainx0_char, trainy0, _, _ = Get_Ner_bioes_and_Char(
params.dataf, words, tagger, char_dic)
train = trainx0, trainy0, trainx0_char
devx0, devx0_char, devy0, params.devrawx, params.devpos = Get_Ner_bioes_and_Char(
params.dev, words, tagger, char_dic)
dev = devx0, devy0, devx0_char
print devy0[:10]
print 'dev set', len(devx0)
testx0, testx0_char, testy0, params.testrawx, params.testpos = Get_Ner_bioes_and_Char(
params.test, words, tagger, char_dic)
test = testx0, testy0, testx0_char
print 'test set', len(testx0)
#print Y
print "Using Training Data" + params.dataf
print "Using Word Embeddings with Dimension " + str(params.embedsize)
print "Saving models to: " + params.outfile
tm = CRF_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
def Base(eta, l2, num_filters, inf, hidden_size):
params.outfile = 'base_ner_inf_'
params.dataf = '../ner_data/eng.train.bioes.conll'
params.dev = '../ner_data/eng.dev.bioes.conll'
params.test = '../ner_data/eng.test.bioes.conll'
params.batchsize = 10
params.hidden = hidden_size
params.embedsize = 100
params.eta = eta
params.L2 = l2
params.dropout = 1
params.emb = 1
params.inf = inf
params.char_embedd_dim = 30
params.num_filters = num_filters
params.en_hidden_size = hidden_size
params.de_hidden_size = hidden_size
params.lstm_layers_num = 1
params.num_labels = 17
params.layers_num = 3
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
tagger = getTagger('../ner_data/ner_bioes')
params.taggerlist = getTaggerlist('../ner_data/ner_bioes')
print tagger
char_dic = getTagger('../ner_data/char_dic')
params.char_dic = char_dic
scale = np.sqrt(3.0 / params.char_embedd_dim)
char_embedd_table = np.random.uniform(
-scale, scale,
[len(char_dic), params.char_embedd_dim]).astype(theano.config.floatX)
params.taggerlist = getTaggerlist('../ner_data/ner_bioes')
params.outfile = params.outfile + '_dropout_' + str(
params.dropout) + "_LearningRate" + '_' + str(
params.eta) + '_inf_' + str(inf) + '_' + str(l2) + '_' + str(
num_filters) + '_hidden_' + str(hidden_size)
trainx0, trainx0_char, trainy0, _, _ = Get_Ner_bioes_and_Char(
params.dataf, words, tagger, char_dic)
train = trainx0, trainy0, trainx0_char
devx0, devx0_char, devy0, params.devrawx, params.devpos = Get_Ner_bioes_and_Char(
params.dev, words, tagger, char_dic)
dev = devx0, devy0, devx0_char
print devy0[:10]
print 'dev set', len(devx0)
testx0, testx0_char, testy0, params.testrawx, params.testpos = Get_Ner_bioes_and_Char(
params.test, words, tagger, char_dic)
test = testx0, testy0, testx0_char
if (inf == 0) or (inf == 1):
from base_ner_model_selection import base_model
tm = base_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
elif (inf == 2):
from seq2seq_att_ner import Seq2Seq
tm = Seq2Seq(We, char_embedd_table, params)
tm.train(train, dev, test, params)
elif (inf == 3):
from self_att_ner import Transformer
tm = Transformer(We, char_embedd_table, params)
tm.train(train, dev, test, params)
def Base(eta, l2, inf, hidden_size):
params.outfile = 'h_base_ner_inf_'
params.dataf = '../ner_data/eng.train.bioes.conll'
params.dev = '../ner_data/eng.dev.bioes.conll'
params.test = '../ner_data/eng.test.bioes.conll'
params.batchsize = 10
params.hidden = hidden_size
params.embedsize = 100
params.eta = eta
params.L2 = l2
params.dropout = 0
params.emb =0
params.inf = inf
params.en_hidden_size= hidden_size
params.de_hidden_size= hidden_size
params.lstm_layers_num =1
params.num_labels = 17
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK':0})
a=[0]*len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
tagger = getTagger('../ner_data/ner_bioes')
params.taggerlist = getTaggerlist('../ner_data/ner_bioes')
print tagger
params.outfile = params.outfile+".Batchsize"+'_'+str(params.batchsize)+'_LearningRate_'+str(params.eta)+ '_inf_' +str(inf) + '_' + str(l2) + '_'+ str(hidden_size)
#examples are shuffled data
trainx0, trainy0, _ , _ = Get_Ner_bioes(params.dataf, words, tagger)
traindata = trainx0, trainy0
#N = int(params.frac*len(trainx0))
#traindata = trainx0[:N], trainy0[:N]
devx0, devy0, params.devrawx, params.devpos = Get_Ner_bioes(params.dev, words, tagger)
devdata = devx0, devy0
print devy0[:10]
print 'dev set', len(devx0)
testx0, testy0, params.testrawx, params.testpos = Get_Ner_bioes(params.test, words, tagger)
testdata = testx0, testy0
print 'test set', len(testx0)
#print Y
print "Using Training Data"+params.dataf
print "Using Word Embeddings with Dimension "+str(params.embedsize)
print "Saving models to: "+params.outfile
if (inf ==0) or (inf==1):
tm = base_model(We, params)
tm.train(traindata, devdata, testdata, params)
#elif(inf ==2):
# from seq2seq import Seq2Seq
# tm = Seq2Seq(We, params)
# tm.train(traindata, devdata, testdata, params)
elif(inf ==2):
from seq2seq_att_ner_h import Seq2Seq
#from seq2seq_att_ner_beamsearch import Seq2Seq
#params.de_hidden_size=200
#params.outfile = 'de_hidden_200_' + params.outfile
tm = Seq2Seq(We, params)
tm.train(traindata, devdata, testdata, params)
elif(inf ==3):
#from seq2seq_att_ner import Seq2Seq
from seq2seq_att_ner_h_beamsearch import Seq2Seq
#params.de_hidden_size=200
#params.outfile = 'de_hidden_200_' + params.outfile
tm = Seq2Seq(We, params)
tm.train(traindata, devdata, testdata, params)
elif(inf ==4):
#from seq2seq_att_all import Seq2Seq
from seq2seq_local_att_ner import Seq2Seq
params.window =int(sys.argv[5])
params.outfile = 'local_att_window_' + str(params.window)+ '_attweight_' + sys.argv[6] + params.outfile
tm = Seq2Seq(We, params)
tm.train(traindata, devdata, testdata, params)
def Base(eta, l3, emb, num_filters, inf, hidden_inf):
params.outfile = 'CRF_Inf_NER_'
params.dataf = '../ner_data/eng.train.bioes.conll'
params.dev = '../ner_data/eng.dev.bioes.conll'
params.test = '../ner_data/eng.test.bioes.conll'
params.batchsize = 10
params.hidden = 200
params.embedsize = 100
params.emb = emb
params.eta = eta
params.dropout = 1
params.hidden_inf = hidden_inf
params.char_embedd_dim = 30
params.num_filters = num_filters
params.inf = inf
params.regutype = 0
params.annealing = 1
params.L3 = l3
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
tagger = getTagger('../ner_data/ner_bioes')
params.taggerlist = getTaggerlist('../ner_data/ner_bioes')
char_dic = getTagger('../ner_data/char_dic')
params.char_dic = char_dic
scale = np.sqrt(3.0 / params.char_embedd_dim)
char_embedd_table = np.random.uniform(
-scale, scale,
[len(char_dic), params.char_embedd_dim]).astype(theano.config.floatX)
params.words = words
params.tagger = tagger
params.outfile = params.outfile + ".num_filters" + '_' + str(
num_filters
) + '_dropout_' + str(params.dropout) + '_LearningRate_' + str(
params.eta) + '_' + str(l3) + '_emb_' + str(emb) + '_inf_' + str(
params.inf) + '_hidden_' + str(
params.hidden_inf) + '_annealing_' + str(params.annealing)
trainx0, trainx0_char, trainy0, _, _ = Get_Ner_bioes_and_Char(
params.dataf, words, tagger, char_dic)
train = trainx0, trainy0, trainx0_char
devx0, devx0_char, devy0, params.devrawx, params.devpos = Get_Ner_bioes_and_Char(
params.dev, words, tagger, char_dic)
dev = devx0, devy0, devx0_char
testx0, testx0_char, testy0, params.testrawx, params.testpos = Get_Ner_bioes_and_Char(
params.test, words, tagger, char_dic)
test = testx0, testy0, testx0_char
if (inf == 0) or (inf == 1):
from model_selection_NER_inference import CRF_model
tm = CRF_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
elif (inf == 2):
from model_selection_inference_NER_seq2seq import CRF_seq2seq_model
params.de_hidden_size = hidden_inf
#params.outfile = 'de_hidden_' + str(params.de_hidden_size) + '_' + params.outfile
tm = CRF_seq2seq_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
else:
from model_selection_inference_NER_seq2seq_beamsearch import CRF_seq2seq_model
params.de_hidden_size = hidden_inf
#params.outfile = 'de_hidden_' + str(params.de_hidden_size) + '_' + params.outfile
tm = CRF_seq2seq_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
def Base(eta, l3, emb, batchsize, inf, hidden_inf, tagversion):
params.outfile = 'h_ccctag_CRF_Inf_'
params.dataf = '../supertag_data/train.dat'
params.dev = '../supertag_data/dev.dat'
params.test = '../supertag_data/test.dat'
params.batchsize = batchsize
params.hidden = 512
params.embedsize = 100
params.emb = emb
params.eta = eta
params.dropout = 0
params.hidden_inf = hidden_inf
params.inf = inf
params.regutype = 0
params.annealing = 0
params.L3 = l3
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
if (tagversion == 0):
tagger = getTagger('../supertag_data/tagger_100')
elif (tagversion == 1):
tagger = getTagger('../supertag_data/tagger_200')
else:
tagger = getTagger('../supertag_data/tagger_400')
params.num_labels = len(tagger)
params.words = words
params.tagger = tagger
params.outfile = params.outfile + ".Batchsize" + '_' + str(
params.batchsize) + '_dropout_' + str(
params.dropout) + '_LearningRate_' + str(params.eta) + '_' + str(
l3) + '_emb_' + str(emb) + '_inf_' + str(
params.inf) + '_hidden_' + str(
params.hidden_inf) + '_tagversion_' + str(tagversion)
traindata = getSupertagData(params.dataf, words, tagger)
trainx0, trainy0 = traindata
devdata = getSupertagData(params.dev, words, tagger)
devx0, devy0 = devdata
print 'dev set', len(devx0)
testdata = getSupertagData(params.test, words, tagger)
testx0, testy0 = testdata
print 'test set', len(testx0)
#print Y
print "Using Training Data" + params.dataf
print "Using Word Embeddings with Dimension " + str(params.embedsize)
print "Saving models to: " + params.outfile
#lm = LM_model(params)
#lm.train(trainy0, devy0, params)
if (inf == 0) or (inf == 1):
tm = CRF_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
elif (inf == 2):
#from model_selection_inference_ccctag_seq2seq import CRF_seq2seq_model
from model_selection_inference_ccctag_seq2seq_new import CRF_seq2seq_model
params.de_hidden_size = hidden_inf # 512
params.en_hidden_size = hidden_inf
params.outfile = 'h_ccctag_de_hidden_size_' + str(
params.de_hidden_size) + '_' + params.outfile
tm = CRF_seq2seq_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
else:
#from model_selection_inference_ccctag_seq2seq_beamsearch import CRF_seq2seq_model
from model_selection_inference_ccctag_seq2seq_new_beamsearch import CRF_seq2seq_model
params.de_hidden_size = hidden_inf # 512
params.en_hidden_size = hidden_inf
params.outfile = 'h_ccctag_de_hidden_size_' + str(
params.de_hidden_size) + '_' + params.outfile
tm = CRF_seq2seq_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
def Base(eta, l3, epoches, warmstart):
params.outfile = 'CRF_Inf_NER_'
params.dataf = '../ner_data/eng.train.bioes.conll'
params.dev = '../ner_data/eng.dev.bioes.conll'
params.test = '../ner_data/eng.test.bioes.conll'
emb = 1
params.batchsize = 10
params.hidden = 200
params.embedsize = 100
params.emb = emb
params.eta = eta
params.dropout = 1
params.char_embedd_dim = 30
num_filters = 50
params.num_filters = num_filters
params.epoches = epoches
params.regutype = 0
params.annealing = 0
params.L3 = l3
params.hidden_inf = 200
params.WarmStart = warmstart
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
tagger = getTagger('../ner_data/ner_bioes')
params.taggerlist = getTaggerlist('../ner_data/ner_bioes')
char_dic = getTagger('../ner_data/char_dic')
params.char_dic = char_dic
scale = np.sqrt(3.0 / params.char_embedd_dim)
char_embedd_table = np.random.uniform(
-scale, scale,
[len(char_dic), params.char_embedd_dim]).astype(theano.config.floatX)
params.words = words
params.tagger = tagger
params.outfile = params.outfile + ".num_filters" + '_' + str(
num_filters) + '_LearningRate_' + str(
params.eta) + '_' + str(l3) + '_emb_' + str(emb)
print params.outfile
trainx0, trainx0_char, trainy0, _, _ = Get_Ner_bioes_and_Char(
params.dataf, words, tagger, char_dic)
train = trainx0, trainy0, trainx0_char
devx0, devx0_char, devy0, params.devrawx, params.devpos = Get_Ner_bioes_and_Char(
params.dev, words, tagger, char_dic)
dev = devx0, devy0, devx0_char
testx0, testx0_char, testy0, params.testrawx, params.testpos = Get_Ner_bioes_and_Char(
params.test, words, tagger, char_dic)
test = testx0, testy0, testx0_char
from model_selection_NER_sgd_inference import CRF_model
tm = CRF_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
def Base(eta, l3, batchsize, inf, hidden_inf, tagversion, num_filters):
params.outfile = 'CRF_Inf_ccctag'
params.dataf = '../supertag_data/train.dat'
params.dev = '../supertag_data/dev.dat'
params.test = '../supertag_data/test.dat'
params.batchsize = batchsize
params.hidden = 400
params.embedsize = 100
params.emb = 1
params.eta = eta
params.dropout = 1
params.hidden_inf = hidden_inf
params.char_embedd_dim = 30
params.num_filters = num_filters
params.inf = inf
params.regutype = 0
params.annealing = 0
params.L3 = l3
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
if (tagversion == 0):
tagger = getTagger('../supertag_data/tagger_100')
elif (tagversion == 1):
tagger = getTagger('../supertag_data/tagger_200')
else:
tagger = getTagger('../supertag_data/tagger_400')
params.num_labels = len(tagger)
params.words = words
params.tagger = tagger
char_dic = getTagger('../supertag_data/char_dic')
params.char_dic = char_dic
scale = np.sqrt(3.0 / params.char_embedd_dim)
char_embedd_table = np.random.uniform(
-scale, scale,
[len(char_dic), params.char_embedd_dim]).astype(theano.config.floatX)
params.outfile = params.outfile + ".Batchsize" + '_' + str(
params.batchsize) + '_dropout_' + str(
params.dropout) + '_LearningRate_' + str(params.eta) + '_' + str(
l3) + '_num_filters_' + str(num_filters) + '_inf_' + str(
params.inf) + '_hidden_' + str(
params.hidden_inf) + '_tagversion_' + str(tagversion)
train = getSupertagData_and_Char(params.dataf, words, tagger, char_dic)
dev = getSupertagData_and_Char(params.dev,
words,
tagger,
char_dic,
train=False)
test = getSupertagData_and_Char(params.test,
words,
tagger,
char_dic,
train=False)
if (inf == 0) or (inf == 1):
from model_selection_ccctag_inference import CRF_model
tm = CRF_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
elif (inf == 2):
from model_selection_inference_ccctag_seq2seq import CRF_seq2seq_model
params.de_hidden_size = hidden_inf # 512
params.en_hidden_size = hidden_inf
tm = CRF_seq2seq_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
else:
from model_selection_inference_ccctag_seq2seq_beamsearch import CRF_seq2seq_model
params.de_hidden_size = hidden_inf # 512
params.en_hidden_size = hidden_inf
tm = CRF_seq2seq_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
def Base(eta, epoches):
params.outfile = 'CRF_Inf_ccctag'
params.dataf = '../supertag_data/train.dat'
params.dev = '../supertag_data/dev.dat'
params.test = '../supertag_data/test.dat'
l3 = 0
tagversion = 2
batchsize = 10
params.epoches = epoches
params.batchsize = batchsize
params.hidden = 512
params.embedsize = 100
params.emb = 0
params.eta = eta
params.dropout = 0
num_filters = 30
params.char_embedd_dim = 30
params.num_filters = num_filters
params.regutype = 0
params.annealing = 0
params.L3 = l3
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK':0})
a=[0]*len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
#print We.shape
if (tagversion==0):
tagger = getTagger('../supertag_data/tagger_100')
elif(tagversion==1):
tagger = getTagger('../supertag_data/tagger_200')
else:
tagger = getTagger('../supertag_data/tagger_400')
params.num_labels = len(tagger)
params.words = words
params.tagger = tagger
char_dic = getTagger('../supertag_data/char_dic')
params.char_dic = char_dic
scale = np.sqrt(3.0 / params.char_embedd_dim)
char_embedd_table = np.random.uniform(-scale, scale, [len(char_dic), params.char_embedd_dim]).astype(theano.config.floatX)
params.outfile = params.outfile + '_LearningRate_'+str(params.eta) +'_epoches_'+ str(epoches)
print params.outfile
train = getSupertagData_and_Char(params.dataf, words, tagger, char_dic)
dev = getSupertagData_and_Char(params.dev, words, tagger, char_dic, train=False)
test = getSupertagData_and_Char(params.test, words, tagger, char_dic, train=False)
#print len(test[0])
from model_selection_ccctag_sgd_simple_inference import CRF_model
tm = CRF_model(We, params)
tm.train(train, dev, test, params)
def Base(eta, l3, emb, batchsize, inf, hidden_inf):
params.outfile = 'h_CRF_Inf_NER_'
params.dataf = '../ner_data/eng.train.bioes.conll'
params.dev = '../ner_data/eng.dev.bioes.conll'
params.test = '../ner_data/eng.test.bioes.conll'
params.batchsize = batchsize
params.hidden = 100
params.embedsize = 100
params.emb = emb
params.eta = eta
params.dropout = 0
params.hidden_inf = hidden_inf
params.inf = inf
params.regutype = 0
params.annealing = 0
params.L3 = l3
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
tagger = getTagger('../ner_data/ner_bioes')
params.taggerlist = getTaggerlist('../ner_data/ner_bioes')
params.words = words
params.tagger = tagger
params.outfile = params.outfile + ".Batchsize" + '_' + str(
params.batchsize) + '_dropout_' + str(
params.dropout) + '_LearningRate_' + str(params.eta) + '_' + str(
l3) + '_emb_' + str(emb) + '_inf_' + str(
params.inf) + '_hidden_' + str(params.hidden_inf)
trainx0, trainy0, _, _ = Get_Ner_bioes(params.dataf, words, tagger)
traindata = trainx0, trainy0
devx0, devy0, params.devrawx, params.devpos = Get_Ner_bioes(
params.dev, words, tagger)
devdata = devx0, devy0
print devy0[:10]
print 'dev set', len(devx0)
testx0, testy0, params.testrawx, params.testpos = Get_Ner_bioes(
params.test, words, tagger)
testdata = testx0, testy0
print "Using Training Data" + params.dataf
print "Using Word Embeddings with Dimension " + str(params.embedsize)
print "Saving models to: " + params.outfile
#lm = LM_model(params)
#lm.train(trainy0, devy0, params)
if (inf == 0) or (inf == 1):
from model_selection_NER_inference import CRF_model
tm = CRF_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
elif (inf == 2):
from model_selection_inference_NER_seq2seq_h import CRF_seq2seq_model
params.de_hidden_size = hidden_inf
params.outfile = 'h_de_hidden_' + str(
params.de_hidden_size) + '_' + params.outfile
tm = CRF_seq2seq_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
else:
from model_selection_inference_NER_seq2seq_h_beamsearch import CRF_seq2seq_model
params.de_hidden_size = hidden_inf
params.outfile = 'h_de_hidden_' + str(
params.de_hidden_size) + '_' + params.outfile
tm = CRF_seq2seq_model(We, params)
tm.train(trainx0, trainy0, devx0, devy0, testx0, testy0, params)
def Base(eta, l2, tagversion, hidden, num_filters):
params.outfile = 'ccctag_BiLSTM_CNN_CRF_'
params.dataf = '../supertag_data/train.dat'
params.dev = '../supertag_data/dev.dat'
params.test = '../supertag_data/test.dat'
params.batchsize = 10
params.hidden = hidden
params.embedsize = 100
params.emb = 1
params.eta = eta
params.L2 = l2
params.dropout = 1
params.tagversion = tagversion
params.char_embedd_dim = 30
params.num_filters = num_filters
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
if (tagversion == 0):
tagger = getTagger('../supertag_data/tagger_100')
elif (tagversion == 1):
tagger = getTagger('../supertag_data/tagger_200')
else:
tagger = getTagger('../supertag_data/tagger_400')
params.num_labels = len(tagger)
print len(tagger)
char_dic = getTagger('../supertag_data/char_dic')
params.char_dic = char_dic
scale = np.sqrt(3.0 / params.char_embedd_dim)
char_embedd_table = np.random.uniform(
-scale, scale,
[len(char_dic), params.char_embedd_dim]).astype(theano.config.floatX)
params.outfile = params.outfile + "num_filters_" + str(
num_filters
) + '_dropout_' + str(params.dropout) + "_LearningRate" + '_' + str(
params.eta) + '_' + str(l2) + '_' + str(hidden) + '_emb_' + str(
params.emb) + '_tagversoin_' + str(tagversion)
train = getSupertagData_and_Char(params.dataf, words, tagger, char_dic)
dev = getSupertagData_and_Char(params.dev,
words,
tagger,
char_dic,
train=False)
test = getSupertagData_and_Char(params.test,
words,
tagger,
char_dic,
train=False)
#print Y
print "Using Training Data" + params.dataf
print "Using Word Embeddings with Dimension " + str(params.embedsize)
print "Saving models to: " + params.outfile
tm = CRF_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
def Base(eta, l2, inf, tagversion, hidden):
params.outfile = 'h_base_ccctag_inf_'
params.dataf = '../supertag_data/train.dat'
params.dev = '../supertag_data/dev.dat'
params.test = '../supertag_data/test.dat'
params.batchsize = 10
params.hidden = hidden
params.embedsize = 100
params.eta = eta
params.L2 = l2
params.dropout = 0
params.emb = 0
params.inf = inf
params.en_hidden_size = hidden
params.de_hidden_size = hidden
params.lstm_layers_num = 1
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
if (tagversion == 0):
tagger = getTagger('../supertag_data/tagger_100')
elif (tagversion == 1):
tagger = getTagger('../supertag_data/tagger_200')
else:
tagger = getTagger('../supertag_data/tagger_400')
params.num_labels = len(tagger)
params.outfile = params.outfile + ".Batchsize" + '_' + str(
params.batchsize) + '_LearningRate_' + str(
params.eta) + '_inf_' + str(inf) + '_hidden_' + str(
params.hidden) + '_' + str(tagversion) + '_' + str(l2)
#examples are shuffled data
traindata = getSupertagData(params.dataf, words, tagger)
trainx0, trainy0 = traindata
devdata = getSupertagData(params.dev, words, tagger, train=False)
devx0, devy0 = devdata
print 'dev set', len(devx0)
testdata = getSupertagData(params.test, words, tagger, train=False)
testx0, testy0 = testdata
print 'test set', len(testx0)
print "Using Training Data" + params.dataf
print "Using Word Embeddings with Dimension " + str(params.embedsize)
print "Saving models to: " + params.outfile
if (inf == 0) or (inf == 1):
tm = base_model(We, params)
tm.train(traindata, devdata, testdata, params)
#elif(inf ==2):
# from seq2seq import Seq2Seq
# tm = Seq2Seq(We, params)
# tm.train(traindata, devdata, testdata, params)
elif (inf == 2):
#from seq2seq_att_pos import Seq2Seq
from seq2seq_att_pos_new import Seq2Seq
tm = Seq2Seq(We, params)
tm.train(traindata, devdata, testdata, params)
elif (inf == 3):
##from seq2seq_att_pos_beamsearch import Seq2Seq
from seq2seq_att_pos_new_beamsearch import Seq2Seq
tm = Seq2Seq(We, params)
tm.train(traindata, devdata, testdata, params)
def Base(eta, l2, num_filters, inf, tagversion, hidden):
params.outfile = 'base_ccctag_inf_'
params.dataf = '../supertag_data/train.dat'
params.dev = '../supertag_data/dev.dat'
params.test = '../supertag_data/test.dat'
params.batchsize = 10
params.hidden = hidden
params.embedsize = 100
params.eta = eta
params.L2 = l2
params.dropout = 1
params.emb = 1
params.inf = inf
params.char_embedd_dim = 30
params.num_filters = num_filters
params.en_hidden_size = hidden
params.de_hidden_size = hidden
params.lstm_layers_num = 1
(words, We) = getGloveWordmap('../embedding/glove.6B.100d.txt')
words.update({'UUUNKKK': 0})
a = [0] * len(We[0])
newWe = []
newWe.append(a)
We = newWe + We
We = np.asarray(We).astype('float32')
print We.shape
if (tagversion == 0):
tagger = getTagger('../supertag_data/tagger_100')
elif (tagversion == 1):
tagger = getTagger('../supertag_data/tagger_200')
else:
tagger = getTagger('../supertag_data/tagger_400')
params.num_labels = len(tagger)
char_dic = getTagger('../supertag_data/char_dic')
params.char_dic = char_dic
scale = np.sqrt(3.0 / params.char_embedd_dim)
char_embedd_table = np.random.uniform(
-scale, scale,
[len(char_dic), params.char_embedd_dim]).astype(theano.config.floatX)
params.outfile = params.outfile + ".num_filters_" + str(
num_filters) + '_LearningRate_' + str(
params.eta) + '_inf_' + str(inf) + '_hidden_' + str(
params.hidden) + '_' + str(tagversion) + '_' + str(l2)
#examples are shuffled data
train = getSupertagData_and_Char(params.dataf, words, tagger, char_dic)
dev = getSupertagData_and_Char(params.dev,
words,
tagger,
char_dic,
train=False)
test = getSupertagData_and_Char(params.test,
words,
tagger,
char_dic,
train=False)
if (inf == 0) or (inf == 1):
from base_model_selection_ccctag import base_model
tm = base_model(We, char_embedd_table, params)
tm.train(train, dev, test, params)
elif (inf == 2):
from seq2seq_att_pos import Seq2Seq
#from seq2seq_att_pos_new import Seq2Seq
tm = Seq2Seq(We, char_embedd_table, params)
tm.train(train, dev, test, params)
elif (inf == 3):
##from seq2seq_att_pos_beamsearch import Seq2Seq
from seq2seq_att_pos_new_beamsearch import Seq2Seq
tm = Seq2Seq(We, params)
tm.train(traindata, devdata, testdata, params)