def load_resume_ner(path, char_embedding_path=None, bigram_embedding_path=None, index_token=True,
char_min_freq=1, bigram_min_freq=1, only_train_min_freq=0, train_dataset_rate=1.0):
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
train_dataset_rate_suffix = '' if train_dataset_rate == 1.0 else f'_{train_dataset_rate}'
train_path = os.path.join(path, f'train.char.bmoes{train_dataset_rate_suffix}')
dev_path = os.path.join(path, 'dev.char.bmoes')
test_path = os.path.join(path, 'test.char.bmoes')
print(f"load train dataset: {train_path}")
loader = ConllLoader(['chars', 'target'])
train_bundle = loader.load(train_path)
dev_bundle = loader.load(dev_path)
test_bundle = loader.load(test_path)
datasets = dict()
datasets['train'] = train_bundle.datasets['train']
datasets['dev'] = dev_bundle.datasets['train']
datasets['test'] = test_bundle.datasets['train']
datasets['train'].apply_field(get_bigrams, field_name='chars', new_field_name='bigrams')
datasets['dev'].apply_field(get_bigrams, field_name='chars', new_field_name='bigrams')
datasets['test'].apply_field(get_bigrams, field_name='chars', new_field_name='bigrams')
datasets['train'].add_seq_len('chars')
datasets['dev'].add_seq_len('chars')
datasets['test'].add_seq_len('chars')
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
print(datasets.keys())
print("dev:", len(datasets['dev']))
print("test:", len(datasets['test']))
print("train:", len(datasets['train']))
char_vocab.from_dataset(datasets['train'], field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
bigram_vocab.from_dataset(datasets['train'], field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
if index_token:
char_vocab.index_dataset(datasets['train'], datasets['dev'], datasets['test'],
field_name='chars', new_field_name='chars')
bigram_vocab.index_dataset(datasets['train'], datasets['dev'], datasets['test'],
field_name='bigrams', new_field_name='bigrams')
label_vocab.index_dataset(datasets['train'], datasets['dev'], datasets['test'],
field_name='target', new_field_name='target')
vocabs = {}
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
vocabs['bigram'] = bigram_vocab
# TODO: add span_label, attr_start, attr_end
datasets, vocabs = input_with_span_attr(datasets, vocabs)
embeddings = {}
if char_embedding_path is not None:
char_embedding = StaticEmbedding(char_vocab, char_embedding_path, word_dropout=0.01,
min_freq=char_min_freq, only_train_min_freq=only_train_min_freq)
embeddings['char'] = char_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(bigram_vocab, bigram_embedding_path, word_dropout=0.01,
min_freq=bigram_min_freq, only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_toy_ner(path, char_embedding_path=None, bigram_embedding_path=None, index_token=True, train_clip=False):
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
train_path = os.path.join(path, 'toy_train.bmoes')
dev_path = os.path.join(path, 'toy_dev.bmoes')
test_path = os.path.join(path, 'toy_test.bmoes')
loader = ConllLoader(['chars', 'target'])
train_bundle = loader.load(train_path)
dev_bundle = loader.load(dev_path)
test_bundle = loader.load(test_path)
datasets = dict()
datasets['train'] = train_bundle.datasets['train']
datasets['dev'] = dev_bundle.datasets['train']
datasets['test'] = test_bundle.datasets['train']
datasets['train'].apply_field(get_bigrams, field_name='chars', new_field_name='bigrams')
datasets['dev'].apply_field(get_bigrams, field_name='chars', new_field_name='bigrams')
datasets['test'].apply_field(get_bigrams, field_name='chars', new_field_name='bigrams')
datasets['train'].add_seq_len('chars')
datasets['dev'].add_seq_len('chars')
datasets['test'].add_seq_len('chars')
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary(padding=None, unknown=None)
print(datasets.keys())
print("dev:", len(datasets['dev']))
print("test:", len(datasets['test']))
print("train:", len(datasets['train']))
char_vocab.from_dataset(datasets['train'], field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
bigram_vocab.from_dataset(datasets['train'], field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
if index_token:
char_vocab.index_dataset(datasets['train'], datasets['dev'], datasets['test'],
field_name='chars', new_field_name='chars')
bigram_vocab.index_dataset(datasets['train'], datasets['dev'], datasets['test'],
field_name='bigrams', new_field_name='bigrams')
label_vocab.index_dataset(datasets['train'], datasets['dev'], datasets['test'],
field_name='target', new_field_name='target')
vocabs = {}
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
vocabs['bigram'] = bigram_vocab
vocabs['label'] = label_vocab
# TODO: add span_label, attr_start, attr_end
datasets, vocabs = input_with_span_attr(datasets, vocabs)
embeddings = {}
if char_embedding_path is not None:
char_embedding = StaticEmbedding(char_vocab, char_embedding_path, word_dropout=0.01, )
embeddings['char'] = char_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(bigram_vocab, bigram_embedding_path, word_dropout=0.01)
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_weibo_ner(path, unigram_embedding_path=None, bigram_embedding_path=None, index_token=True,
char_min_freq=1, bigram_min_freq=1, only_train_min_freq=0, char_word_dropout=0.01, train_dataset_rate=1.0):
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
loader = ConllLoader(['chars', 'target'])
# bundle = loader.load(path)
#
# datasets = bundle.datasets
# print(datasets['train'][:5])
train_dataset_rate_suffix = '' if train_dataset_rate == 1.0 else f'_{train_dataset_rate}'
train_path = os.path.join(path, f'train.char.bmoes{train_dataset_rate_suffix}')
print(f"load train dataset: {train_path}")
dev_path = os.path.join(path, 'dev.char.bmoes')
test_path = os.path.join(path, 'test.char.bmoes')
paths = {}
paths['train'] = train_path
paths['dev'] = dev_path
paths['test'] = test_path
datasets = {}
for k, v in paths.items():
bundle = loader.load(v)
datasets[k] = bundle.datasets['train']
for k, v in datasets.items():
print('{}:{}'.format(k, len(v)))
# print(*list(datasets.keys()))
vocabs = {}
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
# TODO: add span_label, attr_start, attr_end
datasets, vocabs = input_with_span_attr(datasets, vocabs)
print(datasets.keys())
print("dev:", len(datasets['dev']))
print("test:", len(datasets['test']))
print("train:", len(datasets['train']))
for k, v in datasets.items():
# ignore the word segmentation tag
v.apply_field(lambda x: [w[0] for w in x], 'chars', 'chars')
v.apply_field(get_bigrams, 'chars', 'bigrams')
char_vocab.from_dataset(datasets['train'], field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
print('label_vocab:{}\n{}'.format(len(label_vocab), label_vocab.idx2word))
for k, v in datasets.items():
# v.set_pad_val('target',-100)
v.add_seq_len('chars', new_field_name='seq_len')
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
bigram_vocab.from_dataset(datasets['train'], field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if index_token:
char_vocab.index_dataset(*list(datasets.values()), field_name='chars', new_field_name='chars')
bigram_vocab.index_dataset(*list(datasets.values()), field_name='bigrams', new_field_name='bigrams')
label_vocab.index_dataset(*list(datasets.values()), field_name='target', new_field_name='target')
# for k,v in datasets.items():
# v.set_input('chars','bigrams','seq_len','target')
# v.set_target('target','seq_len')
vocabs['bigram'] = bigram_vocab
embeddings = {}
if unigram_embedding_path is not None:
unigram_embedding = StaticEmbedding(char_vocab, model_dir_or_name=unigram_embedding_path,
word_dropout=char_word_dropout,
min_freq=char_min_freq, only_train_min_freq=only_train_min_freq, )
embeddings['char'] = unigram_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(bigram_vocab, model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
min_freq=bigram_min_freq, only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
print(f"train: {len(datasets['train'])}; dev: {len(datasets['dev'])}; test: {len(datasets['test'])}")
return datasets, vocabs, embeddings
def load_weibo_ner(path,
unigram_embedding_path=None,
bigram_embedding_path=None,
index_token=True,
normlize={
'char': True,
'bigram': True,
'word': False
}):
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
loader = ConllLoader(['chars', 'target'])
bundle = loader.load(path)
datasets = bundle.datasets
for k, v in datasets.items():
print('{}:{}'.format(k, len(v)))
# print(*list(datasets.keys()))
vocabs = {}
word_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary(padding=None, unknown=None)
for k, v in datasets.items():
# ignore the word segmentation tag
v.apply_field(lambda x: [w[0] for w in x], 'chars', 'chars')
v.apply_field(get_bigrams, 'chars', 'bigrams')
word_vocab.from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
print('label_vocab:{}\n{}'.format(len(label_vocab), label_vocab.idx2word))
for k, v in datasets.items():
# v.set_pad_val('target',-100)
v.add_seq_len('chars', new_field_name='seq_len')
vocabs['char'] = word_vocab
vocabs['label'] = label_vocab
bigram_vocab.from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if index_token:
word_vocab.index_dataset(*list(datasets.values()),
field_name='raw_words',
new_field_name='words')
bigram_vocab.index_dataset(*list(datasets.values()),
field_name='raw_bigrams',
new_field_name='bigrams')
label_vocab.index_dataset(*list(datasets.values()),
field_name='raw_target',
new_field_name='target')
# for k,v in datasets.items():
# v.set_input('chars','bigrams','seq_len','target')
# v.set_target('target','seq_len')
vocabs['bigram'] = bigram_vocab
embeddings = {}
if unigram_embedding_path is not None:
unigram_embedding = StaticEmbedding(
word_vocab,
model_dir_or_name=unigram_embedding_path,
word_dropout=0.01,
normalize=normlize['char'])
embeddings['char'] = unigram_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(
bigram_vocab,
model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
normalize=normlize['bigram'])
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_resume_ner(path,
char_embedding_path=None,
bigram_embedding_path=None,
index_token=True,
normalize={
'char': True,
'bigram': True,
'word': False
}):
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
train_path = os.path.join(path, 'train.char.bmes')
dev_path = os.path.join(path, 'dev.char.bmes')
test_path = os.path.join(path, 'test.char.bmes')
loader = ConllLoader(['chars', 'target'])
train_bundle = loader.load(train_path)
dev_bundle = loader.load(dev_path)
test_bundle = loader.load(test_path)
datasets = dict()
datasets['train'] = train_bundle.datasets['train']
datasets['dev'] = dev_bundle.datasets['train']
datasets['test'] = test_bundle.datasets['train']
datasets['train'].apply_field(get_bigrams,
field_name='chars',
new_field_name='bigrams')
datasets['dev'].apply_field(get_bigrams,
field_name='chars',
new_field_name='bigrams')
datasets['test'].apply_field(get_bigrams,
field_name='chars',
new_field_name='bigrams')
datasets['train'].add_seq_len('chars')
datasets['dev'].add_seq_len('chars')
datasets['test'].add_seq_len('chars')
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary(padding=None, unknown=None)
print(datasets.keys())
print(len(datasets['dev']))
print(len(datasets['test']))
print(len(datasets['train']))
char_vocab.from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
bigram_vocab.from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
if index_token:
char_vocab.index_dataset(datasets['train'],
datasets['dev'],
datasets['test'],
field_name='chars',
new_field_name='chars')
bigram_vocab.index_dataset(datasets['train'],
datasets['dev'],
datasets['test'],
field_name='bigrams',
new_field_name='bigrams')
label_vocab.index_dataset(datasets['train'],
datasets['dev'],
datasets['test'],
field_name='target',
new_field_name='target')
vocabs = {}
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
vocabs['bigram'] = bigram_vocab
embeddings = {}
if char_embedding_path is not None:
char_embedding = StaticEmbedding(char_vocab,
char_embedding_path,
word_dropout=0.01,
normalize=normalize['char'])
embeddings['char'] = char_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(bigram_vocab,
bigram_embedding_path,
word_dropout=0.01,
normalize=normalize['bigram'])
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_conllized_ontonote_POS(path, embedding_path=None):
from fastNLP.io.loader import ConllLoader
header2index = {'words': 3, 'POS': 4, 'NER': 10}
headers = ['words', 'POS']
if 'NER' in headers:
print(
'警告！通过 load_conllized_ontonote 函数读出来的NER标签不是BIOS，是纯粹的conll格式，是错误的！'
)
indexes = list(map(lambda x: header2index[x], headers))
loader = ConllLoader(headers, indexes)
bundle = loader.load(path)
# print(bundle.datasets)
train_set = bundle.datasets['train']
dev_set = bundle.datasets['dev']
test_set = bundle.datasets['test']
# train_set = loader.load(os.path.join(path,'train.txt'))
# dev_set = loader.load(os.path.join(path, 'dev.txt'))
# test_set = loader.load(os.path.join(path, 'test.txt'))
# print(len(train_set))
train_set.add_seq_len('words', 'seq_len')
dev_set.add_seq_len('words', 'seq_len')
test_set.add_seq_len('words', 'seq_len')
# print(dataset['POS'])
vocab = Vocabulary(min_freq=1)
vocab.from_dataset(train_set, field_name='words')
vocab.from_dataset(dev_set, field_name='words')
vocab.from_dataset(test_set, field_name='words')
vocab.index_dataset(train_set, field_name='words')
vocab.index_dataset(dev_set, field_name='words')
vocab.index_dataset(test_set, field_name='words')
label_vocab_dict = {}
for i, h in enumerate(headers):
if h == 'words':
continue
label_vocab_dict[h] = Vocabulary(min_freq=1,
padding=None,
unknown=None)
label_vocab_dict[h].from_dataset(train_set, field_name=h)
label_vocab_dict[h].index_dataset(train_set, field_name=h)
label_vocab_dict[h].index_dataset(dev_set, field_name=h)
label_vocab_dict[h].index_dataset(test_set, field_name=h)
train_set.set_input(Const.INPUT, Const.INPUT_LEN)
train_set.set_target(headers[1])
dev_set.set_input(Const.INPUT, Const.INPUT_LEN)
dev_set.set_target(headers[1])
test_set.set_input(Const.INPUT, Const.INPUT_LEN)
test_set.set_target(headers[1])
if len(headers) > 2:
print('警告：由于任务数量大于1，所以需要每次手动设置target！')
print('train:', len(train_set), 'dev:', len(dev_set), 'test:',
len(test_set))
if embedding_path is not None:
pretrained_embedding = load_word_emb(embedding_path, 300, vocab)
return (train_set, dev_set,
test_set), (vocab, label_vocab_dict), pretrained_embedding
else:
return (train_set, dev_set, test_set), (vocab, label_vocab_dict)
def load_weibo_ner_old(path,
unigram_embedding_path=None,
bigram_embedding_path=None,
index_token=True,
normlize={
'char': True,
'bigram': True,
'word': False
}):
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
loader = ConllLoader(['chars', 'target'])
# from fastNLP.io.file_reader import _read_conll
# from fastNLP.core import Instance,DataSet
# def _load(path):
# ds = DataSet()
# for idx, data in _read_conll(path, indexes=loader.indexes, dropna=loader.dropna,
# encoding='ISO-8859-1'):
# ins = {h: data[i] for i, h in enumerate(loader.headers)}
# ds.append(Instance(**ins))
# return ds
# from fastNLP.io.utils import check_loader_paths
# paths = check_loader_paths(path)
# datasets = {name: _load(path) for name, path in paths.items()}
datasets = {}
train_path = os.path.join(path, 'train.all.bmes')
dev_path = os.path.join(path, 'dev.all.bmes')
test_path = os.path.join(path, 'test.all.bmes')
datasets['train'] = loader.load(train_path).datasets['train']
datasets['dev'] = loader.load(dev_path).datasets['train']
datasets['test'] = loader.load(test_path).datasets['train']
for k, v in datasets.items():
print('{}:{}'.format(k, len(v)))
vocabs = {}
word_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary(padding=None, unknown=None)
for k, v in datasets.items():
# ignore the word segmentation tag
v.apply_field(lambda x: [w[0] for w in x], 'chars', 'chars')
v.apply_field(get_bigrams, 'chars', 'bigrams')
word_vocab.from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
print('label_vocab:{}\n{}'.format(len(label_vocab), label_vocab.idx2word))
for k, v in datasets.items():
# v.set_pad_val('target',-100)
v.add_seq_len('chars', new_field_name='seq_len')
vocabs['char'] = word_vocab
vocabs['label'] = label_vocab
bigram_vocab.from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if index_token:
word_vocab.index_dataset(*list(datasets.values()),
field_name='raw_words',
new_field_name='words')
bigram_vocab.index_dataset(*list(datasets.values()),
field_name='raw_bigrams',
new_field_name='bigrams')
label_vocab.index_dataset(*list(datasets.values()),
field_name='raw_target',
new_field_name='target')
# for k,v in datasets.items():
# v.set_input('chars','bigrams','seq_len','target')
# v.set_target('target','seq_len')
vocabs['bigram'] = bigram_vocab
embeddings = {}
if unigram_embedding_path is not None:
unigram_embedding = StaticEmbedding(
word_vocab,
model_dir_or_name=unigram_embedding_path,
word_dropout=0.01,
normalize=normlize['char'])
embeddings['char'] = unigram_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(
bigram_vocab,
model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
normalize=normlize['bigram'])
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_msra_ner_1(path,
char_embedding_path=None,
bigram_embedding_path=None,
index_token=True,
train_clip=False,
char_min_freq=1,
bigram_min_freq=1,
only_train_min_freq=0):
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
if train_clip:
train_path = os.path.join(path, 'train_dev.char.bmes_clip1')
test_path = os.path.join(path, 'test.char.bmes_clip1')
else:
train_path = os.path.join(path, 'train_dev.char.bmes')
test_path = os.path.join(path, 'test.char.bmes')
loader = ConllLoader(['chars', 'target'])
train_bundle = loader.load(train_path)
test_bundle = loader.load(test_path)
datasets = dict()
datasets['train'] = train_bundle.datasets['train']
datasets['test'] = test_bundle.datasets['train']
datasets['train'].apply_field(get_bigrams,
field_name='chars',
new_field_name='bigrams')
datasets['test'].apply_field(get_bigrams,
field_name='chars',
new_field_name='bigrams')
datasets['train'].add_seq_len('chars')
datasets['test'].add_seq_len('chars')
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
print(datasets.keys())
# print(len(datasets['dev']))
print(len(datasets['test']))
print(len(datasets['train']))
char_vocab.from_dataset(datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['test']])
bigram_vocab.from_dataset(datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
if index_token:
char_vocab.index_dataset(datasets['train'],
datasets['test'],
field_name='chars',
new_field_name='chars')
bigram_vocab.index_dataset(datasets['train'],
datasets['test'],
field_name='bigrams',
new_field_name='bigrams')
label_vocab.index_dataset(datasets['train'],
datasets['test'],
field_name='target',
new_field_name='target')
vocabs = {}
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
vocabs['bigram'] = bigram_vocab
vocabs['label'] = label_vocab
embeddings = {}
if char_embedding_path is not None:
char_embedding = StaticEmbedding(
char_vocab,
char_embedding_path,
word_dropout=0.01,
min_freq=char_min_freq,
only_train_min_freq=only_train_min_freq)
embeddings['char'] = char_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(
bigram_vocab,
bigram_embedding_path,
word_dropout=0.01,
min_freq=bigram_min_freq,
only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_weibo_ner(path,
unigram_embedding_path=None,
bigram_embedding_path=None,
index_token=True,
char_min_freq=1,
bigram_min_freq=1,
only_train_min_freq=0,
char_word_dropout=0.01):
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
loader = ConllLoader(['chars', 'target'])
train_path = os.path.join(path, 'weiboNER_2nd_conll.train')
dev_path = os.path.join(path, 'weiboNER_2nd_conll.dev')
test_path = os.path.join(path, 'weiboNER_2nd_conll.test')
paths = {}
paths['train'] = train_path
paths['dev'] = dev_path
paths['test'] = test_path
datasets = {}
for k, v in paths.items():
bundle = loader.load(v)
datasets[k] = bundle.datasets['train']
for k, v in datasets.items():
print('{}:{}'.format(k, len(v)))
# print(*list(datasets.keys()))
vocabs = {}
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
for k, v in datasets.items():
# ignore the word segmentation tag
v.apply_field(lambda x: [w[0] for w in x], 'chars', 'chars')
v.apply_field(get_bigrams, 'chars', 'bigrams')
char_vocab.from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
print('label_vocab:{}\n{}'.format(len(label_vocab), label_vocab.idx2word))
for k, v in datasets.items():
# v.set_pad_val('target',-100)
v.add_seq_len('chars', new_field_name='seq_len')
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
bigram_vocab.from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if index_token:
char_vocab.index_dataset(*list(datasets.values()),
field_name='chars',
new_field_name='chars')
bigram_vocab.index_dataset(*list(datasets.values()),
field_name='bigrams',
new_field_name='bigrams')
label_vocab.index_dataset(*list(datasets.values()),
field_name='target',
new_field_name='target')
# for k,v in datasets.items():
# v.set_input('chars','bigrams','seq_len','target')
# v.set_target('target','seq_len')
vocabs['bigram'] = bigram_vocab
embeddings = {}
if unigram_embedding_path is not None:
unigram_embedding = StaticEmbedding(
char_vocab,
model_dir_or_name=unigram_embedding_path,
word_dropout=char_word_dropout,
min_freq=char_min_freq,
only_train_min_freq=only_train_min_freq,
)
embeddings['char'] = unigram_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(
bigram_vocab,
model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
min_freq=bigram_min_freq,
only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_weibo_ner(path,
unigram_embedding_path=None,
bigram_embedding_path=None,
index_token=True,
char_min_freq=1,
bigram_min_freq=1,
only_train_min_freq=0,
char_word_dropout=0.01):
"""
加载微博语料，并缓存数据
:param path: 微博数据集的路径，文件夹
:param unigram_embedding_path: 如果存在，那么使用unigram embedding
:param bigram_embedding_path: 如果存在，使用bigram embedding，可以和unigram同时使用
:param index_token:
:param char_min_freq:
:param bigram_min_freq:
:param only_train_min_freq:
:param char_word_dropout:
:return:
"""
from fastNLP.io.loader import ConllLoader
from utils import get_bigrams
#设定数据集的headers是chars和target，即读取的第一列是chars，第二列是target
loader = ConllLoader(headers=['chars', 'target'])
# bundle = loader.load(path)
#
# datasets = bundle.datasets
# print(datasets['train'][:5])
train_path = os.path.join(path, 'train.conll')
dev_path = os.path.join(path, 'dev.conll')
test_path = os.path.join(path, 'test.conll')
paths = {}
paths['train'] = train_path
paths['dev'] = dev_path
paths['test'] = test_path
datasets = {}
for k, v in paths.items():
bundle = loader.load(v)
datasets[k] = bundle.datasets['train']
print("样本集的样本个数信息如下：")
for k, v in datasets.items():
print('{}:{}'.format(k, len(v)))
# print(*list(datasets.keys()))
vocabs = {}
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
for k, v in datasets.items():
# ignore the word segmentation tag
v.apply_field(lambda x: [w[0] for w in x], 'chars', 'chars')
v.apply_field(get_bigrams, 'chars', 'bigrams')
char_vocab.from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
print('label_vocab:{}\n{}'.format(len(label_vocab), label_vocab.idx2word))
for k, v in datasets.items():
# v.set_pad_val('target',-100)
v.add_seq_len('chars', new_field_name='seq_len')
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
bigram_vocab.from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if index_token:
char_vocab.index_dataset(*list(datasets.values()),
field_name='chars',
new_field_name='chars')
bigram_vocab.index_dataset(*list(datasets.values()),
field_name='bigrams',
new_field_name='bigrams')
label_vocab.index_dataset(*list(datasets.values()),
field_name='target',
new_field_name='target')
# for k,v in datasets.items():
# v.set_input('chars','bigrams','seq_len','target')
# v.set_target('target','seq_len')
vocabs['bigram'] = bigram_vocab
embeddings = {}
if unigram_embedding_path is not None:
unigram_embedding = StaticEmbedding(
char_vocab,
model_dir_or_name=unigram_embedding_path,
word_dropout=char_word_dropout,
min_freq=char_min_freq,
only_train_min_freq=only_train_min_freq,
)
embeddings['char'] = unigram_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(
bigram_vocab,
model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
min_freq=bigram_min_freq,
only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_resume_ner(path,char_embedding_path=None,bigram_embedding_path=None,index_token=True,
char_min_freq=1,bigram_min_freq=1,only_train_min_freq=0):
# print('*' * 40)
# print(bigram_embedding_path)
train_path = os.path.join(path,'train.char.bmes')
dev_path = os.path.join(path,'dev.char.bmes')
test_path = os.path.join(path,'test.char.bmes')
loader = ConllLoader(['chars','target'])
# | ['爱', '财', '不', '分', '古', '今', '，', '除... | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O',... |
train_bundle = loader.load(train_path)
dev_bundle = loader.load(dev_path)
test_bundle = loader.load(test_path)
datasets = dict()
datasets['train'] = train_bundle.datasets['train']
datasets['dev'] = dev_bundle.datasets['train']
datasets['test'] = test_bundle.datasets['train']
datasets['train'].apply_field(get_bigrams,field_name='chars',new_field_name='bigrams')
datasets['dev'].apply_field(get_bigrams, field_name='chars', new_field_name='bigrams')
datasets['test'].apply_field(get_bigrams, field_name='chars', new_field_name='bigrams')
datasets['train'].add_seq_len('chars')
datasets['dev'].add_seq_len('chars')
datasets['test'].add_seq_len('chars')
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
print(datasets.keys())
print(len(datasets['dev']))
print(len(datasets['test']))
print(len(datasets['train']))
char_vocab.from_dataset(datasets['train'],field_name='chars',
no_create_entry_dataset=[datasets['dev'],datasets['test']] )
bigram_vocab.from_dataset(datasets['train'],field_name='bigrams',
no_create_entry_dataset=[datasets['dev'],datasets['test']])
label_vocab.from_dataset(datasets['train'],field_name='target')
if index_token:
char_vocab.index_dataset(datasets['train'],datasets['dev'],datasets['test'],
field_name='chars',new_field_name='chars')
bigram_vocab.index_dataset(datasets['train'],datasets['dev'],datasets['test'],
field_name='bigrams',new_field_name='bigrams')
label_vocab.index_dataset(datasets['train'],datasets['dev'],datasets['test'],
field_name='target',new_field_name='target')
vocabs = {}
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
vocabs['bigram'] = bigram_vocab
embeddings = {}
if char_embedding_path is not None:
char_embedding = StaticEmbedding(char_vocab,char_embedding_path,word_dropout=0.01,
min_freq=char_min_freq,only_train_min_freq=only_train_min_freq)
embeddings['char'] = char_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(bigram_vocab,bigram_embedding_path,word_dropout=0.01,
min_freq=bigram_min_freq,only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
# embeddings["bigram"] = char_embedding
return datasets,vocabs,embeddings
def load_tianchi_ner(
path,
unigram_embedding_path=None, # yangjie_rich_pretrain_unigram_path
bigram_embedding_path=None, # yangjie_rich_pretrain_bigram_path
index_token=True,
char_min_freq=1,
bigram_min_freq=1,
only_train_min_freq=0,
char_word_dropout=0.01):
# step0.=============================准备数据，诸如数据地址等
loader = ConllLoader(['chars', 'target'])
train_path = os.path.join(path, 'tianchi.train')
dev_path = os.path.join(path, 'tianchi.dev')
test_path = os.path.join(path, 'tianchi.test')
paths = {}
paths['dev'] = dev_path
paths['train'] = train_path
paths['test'] = test_path
# step1.=============================构建datasets
datasets = {} # 字典！！！ 但是需要注意的是：datasets 中的每一项都是一个 DataSet 类的实例
for k, v in paths.items():
bundle = loader.load(v)
datasets[k] = bundle.datasets['train']
for k, v in datasets.items():
print('{}:{}'.format(k, len(v)))
#step2.=============================根据得到的dataset构建字典信息
vocabs = {}
# 需要学习一下 Vocabulary 的使用方法
# urL:https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_2_vocabulary.html
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
# datasets 就3个键值对，分别是 train:[] , dev:[], test:[]
for item in datasets.items():
print(item)
for k, v in datasets.items(): # 处理键值对
# ignore the word segmentation tag
# apply_field() 方法是fastNLP 中的一个处理DataSet 实例的方法
# 传入得chars 参数是干什么的？这是形参filed_name 和 new_field_name 的两个值，这表明没有对列名进行修改，即不扩增列
# 同理，第二个(get_bigrams,'chars','bigrams') 是根据 chars 这个列的值，新建bigrams这一列
v.apply_field(lambda x: [w[0] for w in x], 'chars', 'chars')
v.apply_field(get_bigrams, 'chars',
'bigrams') # 感觉这里的效果就是将连续的两个字拼在一起，也就是所谓的 bigrams
# datasets['train']是一个DataSet 的实例
# 形参no_create_entry_dataset的作用：在建立词表的时候将test与dev就考虑到模型中，这会使得最终的结果更好
# 根据训练数据构建字典信息
char_vocab.from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
bigram_vocab.from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
#char_vocab.from_dataset(datasets['train'],field_name='chars',no_create_entry_dataset=datasets['dev'])
#bigram_vocab.from_dataset(datasets['train'],field_name='bigrams',no_create_entry_dataset=datasets['dev'])
print('label_vocab:{}\n{}'.format(len(label_vocab), label_vocab.idx2word))
for k, v in datasets.items():
# 将使用len()直接对field_name中每个元素作用，将其结果作为sequence length, 并放入new_field_name=seq_len这个field
v.add_seq_len('chars', new_field_name='seq_len')
# 是否将dataset中的每列转为字典中的index
# 我对 *list(datasets.values()) 这个不是很熟悉
if index_token:
char_vocab.index_dataset(*list(datasets.values()),
field_name='chars',
new_field_name='chars')
bigram_vocab.index_dataset(*list(datasets.values()),
field_name='bigrams',
new_field_name='bigrams')
label_vocab.index_dataset(*list(datasets.values()),
field_name='target',
new_field_name='target')
# vocabs 的构造和 datasets 的构造原理都是相同的
# 二者都是字典，不同的键值对应着不同的数据信息
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
vocabs['bigram'] = bigram_vocab
# step3.=============================构建embedding信息
'''有如下几个问题：
01.不是说预训练的embedding 会失去上下文的语义信息吗？为什么这里又用embedding了？
02.这个embedding 和后面的bertEmbedding 有什么区别？
03.需要学习一下 StaticEmbedding()的作用
'''
embeddings = {}
if unigram_embedding_path is not None:
unigram_embedding = StaticEmbedding(
char_vocab,
model_dir_or_name=unigram_embedding_path,
word_dropout=char_word_dropout,
min_freq=char_min_freq,
only_train_min_freq=only_train_min_freq)
# 这里的 unigram_embedding 就是一个实例
embeddings['char'] = unigram_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(
bigram_vocab,
model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
min_freq=bigram_min_freq,
only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_weibo_ner(path,
unigram_embedding_path=None,
bigram_embedding_path=None,
index_token=True,
char_min_freq=1,
bigram_min_freq=1,
only_train_min_freq=0,
char_word_dropout=0.01):
# step0.=============================准备数据，诸如数据地址等
loader = ConllLoader(['chars', 'target'])
train_path = os.path.join(path, 'weiboNER_2nd_conll.train_deseg')
dev_path = os.path.join(path, 'weiboNER_2nd_conll.dev_deseg')
test_path = os.path.join(path, 'weiboNER_2nd_conll.test_deseg')
paths = {}
paths['train'] = train_path
paths['dev'] = dev_path
paths['test'] = test_path
# step1.=============================构建datasets
datasets = {} # 字典！！！ 但是需要注意的是：datasets 中的每一项都是一个(fastNLP)中 DataSet 类的实例
for k, v in paths.items():
bundle = loader.load(v)
# 这里有点儿疑问，为什么是固定的 'train' 作为参数？
# 固定的 train 为参数，是因为bundle 这个实例的设置，它是把数据都放到 train 这个里面了
datasets[k] = bundle.datasets['train']
trainData = datasets['train']
print(type(trainData)) # <class 'fastNLP.core.dataset.DataSet'>
print(len(trainData)) # 1350
print(trainData)
"""
datasets['train'] 中的数据长成下面这样，
+-----------------------------------------------------------+-----------------------------------------------------------+
| chars | target |
+-----------------------------------------------------------+-----------------------------------------------------------+
| ['科', '技', '全', '方', '位', '资', '讯', '智', '能',... | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'... |
| ['对', '，', '输', '给', '一', '个', '女', '人', '，',... | ['O', 'O', 'O', 'O', 'O', 'O', 'B-PER.NOM', 'I-PER.NOM... |
| ['今', '天', '下', '午', '起', '来', '看', '到', '外',... | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'... |
| ['今', '年', '拜', '年', '不', '短', '信', '，', '就',... | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'... |
| ['浑', '身', '酸', '疼', '，', '两', '腿', '无', '力',... | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'... |
| ['明', '显', '紧', '张', '状', '态', '没', '出', '来',... | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'... |
| ['三', '十', '年', '前', '，', '老', '爹', '带', '我',... | ['O', 'O', 'O', 'O', 'O', 'B-PER.NOM', 'I-PER.NOM', 'O... |
| ['好', '活', '动', '呀', '，', '给', '力', '的', '商',... | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'... |
| ['人', '生', '如', '戏', '，', '导', '演', '是', '自',... | ['O', 'O', 'O', 'O', 'O', 'B-PER.NOM', 'I-PER.NOM', 'O... |
| ['听', '说', '小', '米', '开', '卖', '了', '，', '刚',... | ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'... |
| ... | ... |
+-----------------------------------------------------------+-----------------------------------------------------------+
这个是 复旦大学开源工具fastNLP 中DataSet 的类型，其详细文档可参考：https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_1_data_preprocess.html
"""
for k, v in datasets.items():
print('{}:{}'.format(k, len(v)))
# print(*list(datasets.keys()))
#step2.=============================根据得到的dataset构建字典信息
vocabs = {}
# 需要学习一下 Vocabulary 的使用方法
# urL:https://fastnlp.readthedocs.io/zh/latest/tutorials/tutorial_2_vocabulary.html
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
# datasets 就3个键值对，分别是 train:[] , dev:[], test:[]
for item in datasets.items():
print(item)
for k, v in datasets.items(): # 处理键值对
# ignore the word segmentation tag
# apply_field() 方法是fastNLP 中的一个处理DataSet 实例的方法
# 传入得chars 参数是干什么的？这是形参filed_name 和 new_field_name 的两个值，这表明没有对列名进行修改，即不扩增列
# 同理，第二个(get_bigrams,'chars','bigrams') 是根据 chars 这个列的值，新建bigrams这一列
v.apply_field(lambda x: [w[0] for w in x], 'chars', 'chars')
v.apply_field(get_bigrams, 'chars',
'bigrams') # 感觉这里的效果就是将连续的两个字拼在一起，也就是所谓的 bigrams
# datasets['train']是一个DataSet 的实例
char_vocab.from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
label_vocab.from_dataset(datasets['train'], field_name='target')
print('label_vocab:{}\n{}'.format(len(label_vocab), label_vocab.idx2word))
for k, v in datasets.items():
# v.set_pad_val('target',-100)
v.add_seq_len('chars', new_field_name='seq_len')
bigram_vocab.from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if index_token:
char_vocab.index_dataset(*list(datasets.values()),
field_name='chars',
new_field_name='chars')
bigram_vocab.index_dataset(*list(datasets.values()),
field_name='bigrams',
new_field_name='bigrams')
label_vocab.index_dataset(*list(datasets.values()),
field_name='target',
new_field_name='target')
# vocabs 的构造和 datasets 的构造原理都是相同的
# 二者都是字典，不同的键值对应着不同的数据信息
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
vocabs['bigram'] = bigram_vocab
# step3.=============================构建embedding信息
embeddings = {}
if unigram_embedding_path is not None:
unigram_embedding = StaticEmbedding(
char_vocab,
model_dir_or_name=unigram_embedding_path,
word_dropout=char_word_dropout,
min_freq=char_min_freq,
only_train_min_freq=only_train_min_freq,
)
embeddings['char'] = unigram_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(
bigram_vocab,
model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
min_freq=bigram_min_freq,
only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
def load_aicup_ner(
path,
unigram_embedding_path=None,
bigram_embedding_path=None,
char_word_dropout=0.01,
only_train_min_freq=0,
bigram_min_freq=1,
data_type='default',
index_token=True,
char_min_freq=1,
cv=False,
fold=0,
):
vocabs = {}
embeddings = {}
train_path = os.path.join(path, f'fold{fold}', f'train/{data_type}')
dev_path = os.path.join(path, f'fold{fold}', f'dev/{data_type}')
print('-----------------Dataset---------------------')
print('loading data from', train_path,'\nand', dev_path)
loader = ConllLoader(['chars', 'target'])
train = loader.load(train_path)
dev = loader.load(dev_path)
ds = {
'train':train.datasets['train'],
'dev':dev.datasets['train'],
}
ds['aicup_dev'] = get_aicup_devds()
# jieba.enable_paddle()
for ds_name in ds.keys():
ds[ds_name].apply_field(get_bigrams, 'chars', 'bigrams')
ds[ds_name].add_seq_len('chars', new_field_name='seq_len')
ds[ds_name].apply_field(get_pos_tag, 'chars', 'pos_tag')
for k, v in ds.items():
print('{}:{}'.format(k, len(v)))
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
pos_vocab = Vocabulary()
label_vocab = get_label_vocab(data_type)
pos_vocab.from_dataset(*list(ds.values()), field_name='pos_tag')
if cv: no_create_entry_ds = [ds['dev'], ds['aicup_dev']]
else: no_create_entry_ds = [ds['dev'], ds['test'], ds['aicup_dev']]
char_vocab.from_dataset(
ds['train'],
field_name='chars',
no_create_entry_dataset=no_create_entry_ds
)
bigram_vocab.from_dataset(
ds['train'],
field_name='bigrams',
no_create_entry_dataset=no_create_entry_ds
)
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
vocabs['bigram'] = bigram_vocab
vocabs['pos_tag'] = pos_vocab
if index_token:
char_vocab.index_dataset(*list(ds.values()), field_name='chars', new_field_name='chars')
bigram_vocab.index_dataset(*list(ds.values()),field_name='bigrams',new_field_name='bigrams')
label_vocab.index_dataset(*list([ds['train'], ds['dev']]), field_name='target', new_field_name='target')
pos_vocab.index_dataset(*list(ds.values()),field_name='pos_tag', new_field_name='pos_tag')
unigram_embedding = StaticEmbedding(
char_vocab,
model_dir_or_name=unigram_embedding_path,
word_dropout=char_word_dropout,
min_freq=char_min_freq,
only_train_min_freq=only_train_min_freq,
)
bigram_embedding = StaticEmbedding(
bigram_vocab,
model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
min_freq=bigram_min_freq,
only_train_min_freq=only_train_min_freq,
)
embeddings['char'] = unigram_embedding
embeddings['bigram'] = bigram_embedding
print(ds['train'])
print(set([ele[0].split('-')[1] if ele[0]!='O' and ele[0][0]!='<' else ele[0] for ele in list(label_vocab)]))
print('------------------------------------------')
return ds, vocabs, embeddings
# print(words, [tag] * len(words))
if not len(sen) == len(pos_tag):
print(sen)
assert len(sen) == len(pos_tag)
return pos_tag
if __name__ == "__main__":
label = get_label_vocab(data_type='default')
print(label)
exit()
dev_path = os.path.join(aicup_ner_path, f'fold{0}', f'dev/number')
print('-----------------Dataset---------------------')
loader = ConllLoader(['chars', 'target'])
dev = loader.load(dev_path)
print(dev.datasets['train'])
# label_vocab = get_label_vocab('number)
# print(label_vocab)
# print(list(label_vocab))
# pass
# ds, vocabs, embeddings = load_aicup_ner(
# aicup_ner_path,
# yangjie_rich_pretrain_unigram_path,
# yangjie_rich_pretrain_bigram_path,
# index_token=False,
# char_min_freq=1,
# bigram_min_freq=1,
# only_train_min_freq=True,
def load_ner(path,unigram_embedding_path=None,bigram_embedding_path=None,index_token=True, train_path=None, dev_path=None,
char_min_freq=1,bigram_min_freq=1,only_train_min_freq=0,char_word_dropout=0.01, test_path=None, \
logger=None, with_placeholder=True, placeholder_path=None, with_test_a=False, test_a_path=None, label_word2idx=None, \
**kwargs):
loader = ConllLoader(['chars','target'])
# train_path = os.path.join(path,'weiboNER_2nd_conll.train_deseg')
# dev_path = os.path.join(path, 'weiboNER_2nd_conll.dev_deseg')
# test_path = os.path.join(path, 'weiboNER_2nd_conll.test_deseg')
if train_path is None:
train_path = '/ai/223/person/lichunyu/datasets/dataf/seq_label/seq_label.train'
if dev_path is None:
dev_path = '/ai/223/person/lichunyu/datasets/dataf/seq_label/seq_label.test'
# train_path = '/ai/223/person/lichunyu/datasets/dataf/seq_label/seq_label_all_all.train'
# dev_path = '/ai/223/person/lichunyu/datasets/dataf/seq_label/seq_label_test_a_labeled.train'
# train_path = '/ai/223/person/lichunyu/datasets/dataf/test/test_A_text.seq'
# dev_path = '/ai/223/person/lichunyu/datasets/dataf/test/test_A_text.seq'
# test_path = '/ai/223/person/lichunyu/datasets/tmp/test_one.txt'
if test_path is None:
test_path = '/ai/223/person/lichunyu/datasets/dataf/test/test_B_final_text.nonletter'
if placeholder_path is None:
placeholder_path = '/root/all_train.test'
if test_a_path is None:
test_a_path = '/ai/223/person/lichunyu/datasets/df-competition/df-511/test/test_A_text.seq'
paths = {}
paths['train'] = train_path
paths['dev'] = dev_path
paths['test'] = test_path
paths['placeholder'] = placeholder_path
paths['test_a'] = test_a_path
datasets = {}
for k,v in paths.items():
bundle = loader.load(v)
datasets[k] = bundle.datasets['train']
for k,v in datasets.items():
if logger is not None:
logger.info('{}:{}'.format(k,len(v)))
else:
print('{}:{}'.format(k,len(v)))
# print(*list(datasets.keys()))
vocabs = {}
char_vocab = Vocabulary()
bigram_vocab = Vocabulary()
label_vocab = Vocabulary()
for k,v in datasets.items():
# ignore the word segmentation tag
v.apply_field(lambda x: [w[0] for w in x],'chars','chars')
v.apply_field(get_bigrams,'chars','bigrams')
# char_vocab.from_dataset(datasets['train'],field_name='chars',no_create_entry_dataset=[datasets['dev'],datasets['test']])
if with_placeholder is True and with_test_a is False:
char_vocab.from_dataset(datasets['train'],field_name='chars',no_create_entry_dataset=[datasets['dev'], datasets['placeholder']])
elif with_placeholder is True and with_test_a is True:
char_vocab.from_dataset(datasets['train'],field_name='chars',no_create_entry_dataset=[datasets['dev'], datasets['placeholder'], datasets['test_a']])
else:
char_vocab.from_dataset(datasets['train'],field_name='chars',no_create_entry_dataset=[datasets['dev']])
label_vocab.from_dataset(datasets['train'],field_name='target')
if label_word2idx is not None:
label_vocab.word2idx = label_word2idx
if logger is not None:
logger.info('label_vocab:{}\n{}'.format(len(label_vocab),label_vocab.idx2word))
for k,v in datasets.items():
# v.set_pad_val('target',-100)
v.add_seq_len('chars',new_field_name='seq_len')
vocabs['char'] = char_vocab
vocabs['label'] = label_vocab
# bigram_vocab.from_dataset(datasets['train'],field_name='bigrams',no_create_entry_dataset=[datasets['dev'],datasets['test']])
if with_placeholder is True and with_test_a is False:
bigram_vocab.from_dataset(datasets['train'],field_name='bigrams',no_create_entry_dataset=[datasets['dev'], datasets['placeholder']])
elif with_placeholder is True and with_test_a is True:
bigram_vocab.from_dataset(datasets['train'],field_name='bigrams',no_create_entry_dataset=[datasets['dev'], datasets['placeholder'], datasets['test_a']])
print('dataset create with test_a')
else:
bigram_vocab.from_dataset(datasets['train'],field_name='bigrams',no_create_entry_dataset=[datasets['dev']])
if index_token:
char_vocab.index_dataset(*list(datasets.values()), field_name='chars', new_field_name='chars')
bigram_vocab.index_dataset(*list(datasets.values()),field_name='bigrams',new_field_name='bigrams')
label_vocab.index_dataset(*list(datasets.values()), field_name='target', new_field_name='target')
vocabs['bigram'] = bigram_vocab
embeddings = {}
if unigram_embedding_path is not None:
unigram_embedding = StaticEmbedding(char_vocab, model_dir_or_name=unigram_embedding_path,
word_dropout=char_word_dropout,
min_freq=char_min_freq,only_train_min_freq=only_train_min_freq,)
embeddings['char'] = unigram_embedding
if bigram_embedding_path is not None:
bigram_embedding = StaticEmbedding(bigram_vocab, model_dir_or_name=bigram_embedding_path,
word_dropout=0.01,
min_freq=bigram_min_freq,only_train_min_freq=only_train_min_freq)
embeddings['bigram'] = bigram_embedding
return datasets, vocabs, embeddings
