def read_parse_write(elmo: ElmoEmbedder, infile: str, outfile: str, mode: str = "average", batch_size=0) -> None:
"""
Read the input files and write the vectors to the output files
:param elmo: ELMo embedder
:param infile: input files for the sentences
:param outfile: output vector files
:param mode: the mode of elmo vectors
:return:
"""
reader = Reader()
insts = reader.read_txt(infile, -1)
f = open(outfile, 'wb')
all_vecs = []
all_sents = []
for inst in insts:
all_sents.append(inst.input.words)
if batch_size < 1: # Not using batch
for sent in tqdm(all_sents, desc="Elmo Embedding"):
elmo_vecs = elmo.embed_sentence(sent)
vec = parse_sentence(elmo_vecs, mode=mode)
all_vecs.append(vec)
else: # Batched prediction
for elmo_vecs in tqdm(elmo.embed_sentences(all_sents, batch_size=batch_size), desc="Elmo Embedding", total=len(all_sents)):
vec = parse_sentence(elmo_vecs, mode=mode)
all_vecs.append(vec)
print("Finishing embedding ELMo sequences, saving the vector files.")
pickle.dump(all_vecs, f)
f.close()
def read_parse_write(tokenizer: BertTokenizer, bert_client: BertClient,
infile: str, outfile: str, mode) -> None:
"""
Read the input files and write the vectors to the output files
:param bert_client: BertClient
:param infile: input files for the sentences
:param outfile: output vector files
:param mode: the mode of bert word piece
:return:
"""
reader = Reader()
insts = reader.read_txt(infile, -1)
f = open(outfile, 'wb')
all_vecs = []
all_sents = []
for inst in insts:
all_sents.append(inst.input.words)
for sent in tqdm(all_sents, desc="BERT encoding"):
word_piece_tokens, word_to_piece_index = bert_tokenize_words(tokenizer,
sent,
mode=mode)
bert_vec = np.squeeze(bert_client.encode([word_piece_tokens],
is_tokenized=True),
axis=0)[1:-1, :] ## exclude the [CLS] and [SEP]
bert_vec = bert_vec[word_to_piece_index, :]
print(bert_vec.shape)
all_vecs.append(bert_vec)
print("Finishing embedding BERT sequences, saving the vector files.")
pickle.dump(all_vecs, f)
f.close()
def main():
parser = argparse.ArgumentParser(
description="Dependency-Guided LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains = reader.read_conll(conf.train_file, -1, True)
devs = reader.read_conll(conf.dev_file, conf.dev_num, False)
tests = reader.read_conll(conf.test_file, conf.test_num, False)
if conf.context_emb != ContextEmb.none:
print('Loading the {} vectors for all datasets.'.format(
conf.context_emb.name))
conf.context_emb_size = reader.load_elmo_vec(
conf.train_file.replace(".sd", "").replace(".ud", "").replace(
".sud", "").replace(".predsd", "").replace(
".predud", "").replace(".stud", "").replace(".ssd", "") +
"." + conf.context_emb.name + ".vec", trains)
reader.load_elmo_vec(
conf.dev_file.replace(".sd", "").replace(".ud", "").replace(
".sud", "").replace(".predsd", "").replace(
".predud", "").replace(".stud", "").replace(".ssd", "") +
"." + conf.context_emb.name + ".vec", devs)
reader.load_elmo_vec(
conf.test_file.replace(".sd", "").replace(".ud", "").replace(
".sud", "").replace(".predsd", "").replace(
".predud", "").replace(".stud", "").replace(".ssd", "") +
"." + conf.context_emb.name + ".vec", tests)
conf.use_iobes(trains + devs + tests)
conf.build_label_idx(trains)
conf.build_deplabel_idx(trains + devs + tests)
print("# deplabels: ", len(conf.deplabels))
print("dep label 2idx: ", conf.deplabel2idx)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(trains + devs + tests)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
if conf.train_num != -1:
random.shuffle(trains)
trains = trains[:conf.train_num]
learn_from_insts(conf, conf.num_epochs, trains, devs, tests)
else:
## Load the trained model.
test_model(conf, tests)
# pass
print(opt.mode)
def read_parse_write(elmo, infile, outfile, mode):
reader = Reader()
insts = reader.read_conll(infile, -1, True)
f = open(outfile, 'wb')
all_vecs = []
for inst in insts:
vec = parse_sentence(elmo, inst.input.words, mode=mode)
all_vecs.append(vec)
pickle.dump(all_vecs, f)
f.close()
def read_parse_write(elmo, infile, outfile,):
reader = Reader()
insts = reader.read_conll(infile, -1, True)
f = open(outfile, 'wb')
all_vecs = []
for inst in insts:
sent = embed_sent(elmo, inst.input.words)
# np.empty((len(sent)),dtype=np.float32)
arr = []
for token in sent:
# print(token)
# print(token.embedding)
arr.append(np.expand_dims(token.embedding.numpy(), axis=0))
# all_vecs.append(vec)
all_vecs.append(np.concatenate(arr))
pickle.dump(all_vecs, f)
f.close()
def read_parse_write(elmo: ElmoEmbedder, infile: str, outfile: str, mode: str = "average") -> None:
"""
Read the input files and write the vectors to the output files
:param elmo: ELMo embedder
:param infile: input files for the sentences
:param outfile: output vector files
:param mode: the mode of elmo vectors
:return:
"""
reader = Reader()
insts = reader.read_txt(infile, -1)
f = open(outfile, 'wb')
all_vecs = []
for inst in tqdm(insts):
vec = parse_sentence(elmo, inst.input.words, mode=mode)#Remove pos_tags argument for model without additional embeedding for materials
all_vecs.append(vec)
print("Finishing embedding ELMo sequences, saving the vector files.")
pickle.dump(all_vecs, f)
f.close()
def read_parse_write(bert: DistilBertModel,
bert_path: str,
infile: str,
outfile: str,
mode: str = "average",
batch_size=0) -> None:
"""
Read the input files and write the vectors to the output files
:param bert: Bert embedder
:param infile: input files for the sentences
:param outfile: output vector files
:param mode: the mode of elmo vectors
:return:
"""
reader = Reader()
insts = reader.read_txt(infile, -1)
f = open(outfile, 'wb')
all_vecs = []
all_sents = []
for inst in insts:
all_sents.append(inst.input.words)
dataset = CustomDataset(all_sents, bert_path)
batch_size = max(1, batch_size) # make sure batch_size is gt 0
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=4)
for _, (batch, n_pads) in tqdm(enumerate(dataloader)):
with torch.no_grad():
batch = batch.cuda() if CUDA else batch
bert = bert.cuda() if CUDA else bert
bert_batch_vecs = bert(batch)[0].cpu().numpy()
vectors = parse_sentence(bert_batch_vecs, mode=mode)
for j in range(vectors.shape[0]):
all_vecs.append(vectors[j, :-n_pads[j], :])
print("Finishing embedding Bert sequences, saving the vector files.")
pickle.dump(all_vecs, f)
f.close()
def main():
parser = argparse.ArgumentParser(
description="Dependency-Guided LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains = reader.read_conll(conf.train_file, -1, True)
devs = reader.read_conll(conf.dev_file, conf.dev_num, False)
tests = reader.read_conll(conf.test_file, conf.test_num, False)
conf.use_iobes(trains)
conf.build_label_idx(trains)
conf.build_deplabel_idx(trains + devs + tests)
print("# deplabels: ", len(conf.deplabels))
print("dep label 2idx: ", conf.deplabel2idx)
conf.build_word_idx(trains + devs + tests)
conf.build_emb_table()
conf.map_insts_ids(trains)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
if conf.train_num != -1:
random.shuffle(trains)
trains = trains[:conf.train_num]
learn_from_insts(conf, conf.num_epochs, trains)
print(opt.mode)
def main():
TASKS = ['ner_german', 'ner']
USE_DEV = True
char_set = set()
for task in TASKS:
t = __import__(task)
data_list = [t.TRAIN_DATA, t.DEV_DATA, t.TEST_DATA]
char_index, _ = t.create_char_index(data_list)
for k, v in char_index.items():
char_set.add(k)
char_index, char_cnt = {}, 0
for char in char_set:
char_index[char] = char_cnt
char_cnt += 1
for i, task in enumerate(TASKS):
t = __import__(task)
word_index, word_cnt = t.create_word_index(
[t.TRAIN_DATA, t.DEV_DATA, t.TEST_DATA])
wx, y, m = t.read_data(t.TRAIN_DATA, word_index)
if USE_DEV and task == 'ner':
dev_wx, dev_y, dev_m = t.read_data(t.TEST_DATA, word_index)
wx, y, m = np.vstack((wx, dev_wx)), np.vstack(
(y, dev_y)), np.vstack((m, dev_m))
twx, ty, tm = t.read_data(t.DEV_DATA, word_index)
x, cm = t.read_char_data(t.TRAIN_DATA, char_index)
if USE_DEV and task == 'ner':
dev_x, dev_cm = t.read_char_data(t.TEST_DATA, char_index)
x, cm = np.vstack((x, dev_x)), np.vstack((cm, dev_cm))
tx, tcm = t.read_char_data(t.DEV_DATA, char_index)
if task == 'ner':
list_prefix = t.read_list()
gaze = t.read_list_data(t.TRAIN_DATA, list_prefix)
tgaze = t.read_list_data(t.DEV_DATA, list_prefix)
if USE_DEV:
dev_gaze = t.read_list_data(t.TEST_DATA, list_prefix)
gaze = np.vstack((gaze, dev_gaze))
else:
gaze, tgaze = None, None
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains = reader.read_txt(conf.train_file, conf.train_num, True)
devs = reader.read_txt(conf.dev_file, conf.dev_num, False)
tests = reader.read_txt(conf.test_file, conf.test_num, False)
trains_target = reader.read_txt(conf.train_target_file_file,
conf.train_num, True)
if conf.context_emb != ContextEmb.none:
print('Loading the elmo vectors for all datasets.')
conf.context_emb_size = reader.load_elmo_vec(
conf.train_file + "." + conf.context_emb.name + ".vec", trains)
reader.load_elmo_vec(
conf.dev_file + "." + conf.context_emb.name + ".vec", devs)
reader.load_elmo_vec(
conf.test_file + "." + conf.context_emb.name + ".vec", tests)
conf.use_iobes(trains)
conf.use_iobes(devs)
conf.use_iobes(tests)
conf.build_label_idx(trains)
conf.use_iobes(trains_target)
conf.build_label_idx_target(trains_target)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
ids_train = conf.map_insts_ids(trains)
ids_dev = conf.map_insts_ids(devs)
ids_test = conf.map_insts_ids(tests)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
learn_from_insts(conf, conf.num_epochs, trains, devs, tests)
else:
## Load the trained model.
test_model(conf, tests)
# pass
print(opt.mode)
#
# @author: Allan
#
from config.reader import Reader
file = "data/ontonotes/train.sd.conllx"
digit2zero = False
reader = Reader(digit2zero)
insts = reader.read_conll(file, -1, True)
# devs = reader.read_conll(conf.dev_file, conf.dev_num, False)
# tests = reader.read_conll(conf.test_file, conf.test_num, False)
out_dep_label2num = {}
out_doubledep2num = {}
out_word2num = {}
label2idx = {}
def not_entity(label:str):
if label.startswith("B-") or label.startswith("I-"):
return False
return True
def is_entity(label:str):
if label.startswith("B-") or label.startswith("I-"):
return True
return False
def main():
print('Reading arguments')
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
conf_conll = Config_conll(opt)
conf_ontonotes = Config_ontonotes(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains_0 = reader.read_conll(conf.train_file_1, 0, conf.train_num, True)
devs_0 = reader.read_conll(conf.dev_file_1, 0, conf.dev_num, False)
tests_0 = reader.read_conll(conf.test_file_1, 0, conf.test_num, False)
trains_1 = reader.read_conll(conf.train_file_2, 1, conf.train_num, True)
devs_1 = reader.read_conll(conf.dev_file_2, 1, conf.dev_num, False)
tests_1 = reader.read_conll(conf.test_file_2, 1, conf.test_num, False)
trains_all = trains_0 + trains_1
devs_all = devs_0 + devs_1
tests_all = tests_0 + tests_1
if conf.context_emb != ContextEmb.none:
print('Loading the elmo vectors for all datasets.')
conf.context_emb_size = reader.load_elmo_vec(
conf.train_file_1 + "." + conf.context_emb.name + ".vec", trains_1)
reader.load_elmo_vec(
conf.dev_file_1 + "." + conf.context_emb.name + ".vec", devs_1)
reader.load_elmo_vec(
conf.test_file_1 + "." + conf.context_emb.name + ".vec", tests_1)
conf.use_iobes(trains_all)
conf.use_iobes(devs_all)
conf.use_iobes(tests_all)
conf.build_label_idx(trains_all)
conf.build_word_idx(trains_all, devs_all, tests_all)
conf.build_emb_table()
ids_train = conf.map_insts_ids(trains_all)
ids_dev = conf.map_insts_ids(devs_all)
ids_test = conf.map_insts_ids(tests_all)
conf_conll.label_size = conf.label_size_0
conf_conll.label2idx = conf.label2idx_0
conf_conll.idx2labels = conf.idx2labels_0
conf_ontonotes.label_size = conf.label_size_1
conf_ontonotes.label2idx = conf.label2idx_1
conf_ontonotes.idx2labels = conf.idx2labels_1
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
learn_from_insts(conf, conf_conll, conf_ontonotes, conf.num_epochs,
trains_all, devs_all, tests_all)
else:
## Load the trained model.
test_model(conf, tests_all)
# pass
print(opt.mode)
else:
next_entity = output[pos + 1]
if curr_entity.startswith("B-"):
if next_entity.startswith("O") or next_entity.startswith("B-"):
output[pos] = curr_entity.replace("B-", "S-")
elif curr_entity.startswith("I-"):
if next_entity.startswith("O") or next_entity.startswith("B-"):
output[pos] = curr_entity.replace("I-", "E-")
dataset = "ontonotes_chinese"
train = "../data/"+dataset+"/train.sd.conllx"
dev = "../data/"+dataset+"/dev.sd.conllx"
test = "../data/"+dataset+"/test.sd.conllx"
digit2zero = False
reader = Reader(digit2zero)
insts = reader.read_conll(train, -1, True)
insts += reader.read_conll(dev, -1, False)
insts += reader.read_conll(test, -1, False)
use_iobes(insts)
L = 3
def get_spans(output):
output_spans = set()
start = -1
for i in range(len(output)):
if output[i].startswith("B-"):
start = i
if output[i].startswith("E-"):
def nonterms(self):
return list(self.nonterms_iter())
def __eq__(self, other):
return other and self.pos == other.pos and self.children == other.children
def __hash__(self):
return hash((self.pos, self.children))
if __name__ == "__main__":
'''###read the tree
'''
from config.reader import Reader
reader = Reader()
insts = reader.read_conll("../data/abc/train.conllx", number=1)
for inst in insts:
nodes = [Tree(pos) for pos in range(len(inst.input.words))]
root = Tree(-1)
for pos, head in enumerate(inst.input.heads):
if head != -1:
nodes[head].add_child(nodes[pos])
else:
root.add_child(nodes[pos])
inst.nodes = nodes
for node in nodes:
node.sort_children()
print(root.leaves())
for pos, node in enumerate(nodes):