def name_list(self):
num_batches = self.num_data / self.batch_size + 1
total_name_dict = {}
self.network.eval()
# evaluate on test data when better performance detected
tmp_filename = 'tmp/%s_name_list' % (str(uid))
self.writer.start(tmp_filename)
for batch in range(1, num_batches + 1):
word, char, pos, chunk, labels, masks, lengths = conll03_data.get_batch_variable(
self.data_train, self.batch_size, unk_replace=self.unk_replace)
preds, _ = self.network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
self.writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
self.writer.close()
#load file and extract namelist
words = []
tag_list = []
names = []
f = open(tmp_filename, "r")
for line in f:
if line == '\n':
names += get_entities(words, tag_list)
words = []
tag_list = []
else:
line_s = line.split()
words.append(line_s[1])
tag_list.append(line_s[5])
names += get_entities(words, tag_list)
for name in names:
if name in total_name_dict:
total_name_dict[name] += 1
else:
total_name_dict[name] = 1
return set([
name for name, num_occ in total_name_dict.items() if num_occ >= 4
])
def main():
parser = argparse.ArgumentParser(
description='Tuning with bi-directional RNN-CNN-CRF')
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
required=True)
parser.add_argument('--num_epochs',
type=int,
default=100,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN')
parser.add_argument('--tag_space',
type=int,
default=0,
help='Dimension of tag space')
parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
parser.add_argument('--num_filters',
type=int,
default=30,
help='Number of filters in CNN')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--learning_rate',
type=float,
default=0.015,
help='Learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.1,
help='Decay rate of learning rate')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for regularization')
parser.add_argument('--dropout',
choices=['std', 'variational'],
help='type of dropout',
required=True)
parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input embeddings')
parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
parser.add_argument('--bigram',
action='store_true',
help='bi-gram parameter for CRF')
parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument('--embedding',
choices=['glove', 'senna', 'sskip', 'polyglot'],
help='Embedding for words',
required=True)
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument(
'--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument(
'--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument(
'--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
logger = get_logger("NERCRF")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
num_filters = args.num_filters
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
bigram = args.bigram
embedding = args.embedding
embedding_path = args.embedding_dict
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("data/alphabets/ner_crf/", train_path, data_paths=[dev_path, test_path],
embedd_dict=embedd_dict, max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Chunk Alphabet Size: %d" % chunk_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conll03_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = conll03_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu,
volatile=True)
data_test = conll03_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu,
volatile=True)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
initializer = nn.init.xavier_uniform
if args.dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
else:
network = BiVarRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
if use_gpu:
network.cuda()
lr = learning_rate
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
logger.info(
"Network: %s, num_layer=%d, hidden=%d, filter=%d, tag_space=%d, crf=%s"
% (mode, num_layers, hidden_size, num_filters, tag_space,
'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (gamma, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
num_batches = num_data / batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
network.eval()
tmp_filename = 'tmp/%s_dev%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_variable(data_dev, batch_size):
word, char, pos, chunk, labels, masks, lengths = batch
preds, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%' %
(acc, precision, recall, f1))
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = 'tmp/%s_test%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_variable(
data_test, batch_size):
word, char, pos, chunk, labels, masks, lengths = batch
preds, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(
tmp_filename)
print(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
def main():
embedding = 'glove'
embedding_path = '/media/xianyang/OS/workspace/ner/glove.6B/glove.6B.100d.txt'
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("/media/xianyang/OS/workspace/ner/NeuroNLP2/data/alphabets/ner_crf/", None)
char_dim = 30
num_filters = 30
window = 3
mode = 'LSTM'
hidden_size = 256
num_layers = 1
num_labels = ner_alphabet.size()
tag_space = 128
p = 0.5
bigram = True
embedd_dim = 100
use_gpu = False
print(len(word_alphabet.get_content()['instances']))
print(ner_alphabet.get_content())
# writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet, chunk_alphabet, ner_alphabet)
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=None,
p_rnn=p,
bigram=bigram)
network.load_state_dict(torch.load('temp/23df51_model45'))
ner_alphabet.add('B-VEH')
ner_alphabet.add('I-VEH')
ner_alphabet.add('B-WEA')
ner_alphabet.add('I-WEA')
num_new_word = 0
with open('temp/target.train.conll', 'r') as f:
sents = []
sent_buffer = []
for line in f:
if len(line) <= 1:
sents.append(sent_buffer)
sent_buffer = []
else:
id, word, _, _, ner = line.strip().split()
if word_alphabet.get_index(word) == 0:
word_alphabet.add(word)
num_new_word += 1
sent_buffer.append((word_alphabet.get_index(word),
ner_alphabet.get_index(ner)))
print(len(word_alphabet.get_content()['instances']))
print(ner_alphabet.get_content())
init_embed = network.word_embedd.weight.data
init_embed = np.concatenate(
(init_embed, np.zeros((num_new_word, embedd_dim))), axis=0)
network.word_embedd = Embedding(word_alphabet.size(), embedd_dim,
torch.from_numpy(init_embed))
old_crf = network.crf
new_crf = ChainCRF(tag_space, ner_alphabet.size(), bigram=bigram)
trans_matrix = np.zeros((new_crf.num_labels, old_crf.num_labels))
for i in range(old_crf.num_labels):
trans_matrix[i, i] = 1
new_crf.state_nn.weight.data = torch.FloatTensor(
np.dot(trans_matrix, old_crf.state_nn.weight.data))
network.crf = new_crf
target_train_data = conll03_data.read_data_to_variable(
'temp/target.train.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
target_dev_data = conll03_data.read_data_to_variable(
'temp/target.dev.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
target_test_data = conll03_data.read_data_to_variable(
'temp/target.test.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
num_epoch = 50
batch_size = 32
num_data = sum(target_train_data[1])
num_batches = num_data / batch_size + 1
unk_replace = 0.0
# optim = SGD(network.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0, nesterov=True)
optim = Adam(network.parameters(), lr=1e-3)
for epoch in range(1, num_epoch + 1):
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
target_train_data, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
if batch % 20 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d loss: %.4f, time: %.2fs' % (
num_batches, train_err / train_total,
time.time() - start_time)
print(log_info)
num_back = len(log_info)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
os.system('rm temp/output.txt')
writer.start('temp/output.txt')
network.eval()
for batch in conll03_data.iterate_batch_variable(
target_dev_data, batch_size):
word, char, pos, chunk, labels, masks, lengths, _ = batch
preds, _, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate('temp/output.txt')
log_info = 'dev: %f %f %f %f' % (acc, precision, recall, f1)
print(log_info)
if epoch % 10 == 0:
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
os.system('rm temp/output.txt')
writer.start('temp/output.txt')
network.eval()
for batch in conll03_data.iterate_batch_variable(
target_test_data, batch_size):
word, char, pos, chunk, labels, masks, lengths, _ = batch
preds, _, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate('temp/output.txt')
log_info = 'test: %f %f %f %f' % (acc, precision, recall, f1)
print(log_info)
torch.save(network, 'temp/tuned_0905.pt')
alphabet_directory = '0905_alphabet/'
word_alphabet.save(alphabet_directory)
char_alphabet.save(alphabet_directory)
pos_alphabet.save(alphabet_directory)
chunk_alphabet.save(alphabet_directory)
ner_alphabet.save(alphabet_directory)
def train(self):
network = BiRecurrentConvCRF(self.embedd_dim,
self.word_alphabet.size(),
self.char_dim,
self.char_alphabet.size(),
self.num_filters,
self.window,
self.mode,
self.hidden_size,
self.num_layers,
self.num_labels,
tag_space=self.tag_space,
embedd_word=self.word_table,
p_in=self.p_in,
p_out=self.p_out,
p_rnn=self.p_rnn,
bigram=self.bigram,
initializer=self.initializer)
if self.use_gpu:
network.cuda()
optim = SGD(network.parameters(),
lr=self.learning_rate,
momentum=self.momentum,
weight_decay=self.gamma,
nesterov=True)
self.logger.info(
"Network: %s, num_layer=%d, hidden=%d, filter=%d, tag_space=%d, crf=%s"
% (self.mode, self.num_layers, self.hidden_size, self.num_filters,
self.tag_space, 'bigram' if self.bigram else 'unigram'))
self.logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (self.gamma, self.num_data, self.batch_size, self.unk_replace))
self.logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(self.p_in, self.p_out, self.p_rnn))
num_batches = self.num_data / self.batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
for epoch in range(1, self.num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, self.mode, self.dropout, self.learning_rate,
self.decay_rate, self.schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
self.data_train,
self.batch_size,
unk_replace=self.unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total,
time.time() - start_time))
if epoch % self.schedule == 0:
lr = self.learning_rate / (1.0 + epoch * self.decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=self.momentum,
weight_decay=self.gamma,
nesterov=True)
self.network = network
def retrain(train_path, dev_path):
network = torch.load('temp/ner_tuned.pt')
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("ner_alphabet/", None)
num_new_word = 0
with open(train_path, 'r') as f:
sents = []
sent_buffer = []
for line in f:
if len(line) <= 1:
sents.append(sent_buffer)
sent_buffer = []
else:
id, word, _, _, ner = line.strip().split()
if word_alphabet.get_index(word) == 0:
word_alphabet.add(word)
num_new_word += 1
sent_buffer.append((word_alphabet.get_index(word),
ner_alphabet.get_index(ner)))
print('{} new words.'.format(num_new_word))
init_embed = network.word_embedd.weight.data
embedd_dim = init_embed.shape[1]
init_embed = np.concatenate(
(init_embed, np.zeros((num_new_word, embedd_dim))), axis=0)
network.word_embedd = Embedding(word_alphabet.size(), embedd_dim,
torch.from_numpy(init_embed))
target_train_data = conll03_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
num_epoch = 50
batch_size = 20
num_data = sum(target_train_data[1])
num_batches = num_data / batch_size + 1
unk_replace = 0.0
optim = SGD(network.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=0.0,
nesterov=True)
for epoch in range(num_epoch):
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
target_train_data, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total,
time.time() - start_time))
torch.save(network, 'temp/ner_active.pt')
alphabet_directory = 'active_alphabet/'
word_alphabet.save(alphabet_directory)
char_alphabet.save(alphabet_directory)
pos_alphabet.save(alphabet_directory)
chunk_alphabet.save(alphabet_directory)
ner_alphabet.save(alphabet_directory)
target_dev_data = conll03_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
os.system('rm output.txt')
writer.start('output.txt')
network.eval()
for batch in conll03_data.iterate_batch_variable(target_dev_data,
batch_size):
word, char, pos, chunk, labels, masks, lengths, _ = batch
preds, _, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate('output.txt')
print(acc, precision, recall, f1)
return acc, precision, recall, f1
