def equip_chinese_ner_with_lexicon(datasets,
vocabs,
embeddings,
w_list,
word_embedding_path=None,
only_lexicon_in_train=False,
word_char_mix_embedding_path=None,
number_normalized=False,
lattice_min_freq=1,
only_train_min_freq=0):
from fastNLP.core import Vocabulary
def normalize_char(inp):
result = []
for c in inp:
if c.isdigit():
result.append('0')
else:
result.append(c)
return result
def normalize_bigram(inp):
result = []
for bi in inp:
tmp = bi
if tmp[0].isdigit():
tmp = '0' + tmp[:1]
if tmp[1].isdigit():
tmp = tmp[0] + '0'
result.append(tmp)
return result
if number_normalized == 3:
for k, v in datasets.items():
v.apply_field(normalize_char, 'chars', 'chars')
vocabs['char'] = Vocabulary()
vocabs['char'].from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
for k, v in datasets.items():
v.apply_field(normalize_bigram, 'bigrams', 'bigrams')
vocabs['bigram'] = Vocabulary()
vocabs['bigram'].from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if only_lexicon_in_train:
print('已支持只加载在trian中出现过的词汇')
def get_skip_path(chars, w_trie):
sentence = ''.join(chars)
result = w_trie.get_lexicon(sentence)
# print(result)
return result
from V0.utils_ import Trie
from functools import partial
from fastNLP.core import Vocabulary
# from fastNLP.embeddings import StaticEmbedding
from fastNLP_module import StaticEmbedding
from fastNLP import DataSet
a = DataSet()
w_trie = Trie()
for w in w_list:
w_trie.insert(w)
if only_lexicon_in_train:
lexicon_in_train = set()
for s in datasets['train']['chars']:
lexicon_in_s = w_trie.get_lexicon(s)
for s, e, lexicon in lexicon_in_s:
lexicon_in_train.add(''.join(lexicon))
print('lexicon in train:{}'.format(len(lexicon_in_train)))
print('i.e.: {}'.format(list(lexicon_in_train)[:10]))
w_trie = Trie()
for w in lexicon_in_train:
w_trie.insert(w)
import copy
for k, v in datasets.items():
v.apply_field(partial(get_skip_path, w_trie=w_trie), 'chars',
'lexicons')
v.apply_field(copy.copy, 'chars', 'raw_chars')
v.add_seq_len('lexicons', 'lex_num')
v.apply_field(lambda x: list(map(lambda y: y[0], x)), 'lexicons',
'lex_s')
v.apply_field(lambda x: list(map(lambda y: y[1], x)), 'lexicons',
'lex_e')
if number_normalized == 1:
for k, v in datasets.items():
v.apply_field(normalize_char, 'chars', 'chars')
vocabs['char'] = Vocabulary()
vocabs['char'].from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if number_normalized == 2:
for k, v in datasets.items():
v.apply_field(normalize_char, 'chars', 'chars')
vocabs['char'] = Vocabulary()
vocabs['char'].from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
for k, v in datasets.items():
v.apply_field(normalize_bigram, 'bigrams', 'bigrams')
vocabs['bigram'] = Vocabulary()
vocabs['bigram'].from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
def concat(ins):
chars = ins['chars']
lexicons = ins['lexicons']
result = chars + list(map(lambda x: x[2], lexicons))
# print('lexicons:{}'.format(lexicons))
# print('lex_only:{}'.format(list(filter(lambda x:x[2],lexicons))))
# print('result:{}'.format(result))
return result
def get_pos_s(ins):
lex_s = ins['lex_s']
seq_len = ins['seq_len']
pos_s = list(range(seq_len)) + lex_s
return pos_s
def get_pos_e(ins):
lex_e = ins['lex_e']
seq_len = ins['seq_len']
pos_e = list(range(seq_len)) + lex_e
return pos_e
for k, v in datasets.items():
v.apply(concat, new_field_name='lattice')
v.set_input('lattice')
v.apply(get_pos_s, new_field_name='pos_s')
v.apply(get_pos_e, new_field_name='pos_e')
v.set_input('pos_s', 'pos_e')
# print(list(datasets['train'][:10]['lexicons']))
# print(list(datasets['train'][:10]['lattice']))
# print(list(datasets['train'][:10]['lex_s']))
# print(list(datasets['train'][:10]['lex_e']))
# print(list(datasets['train'][:10]['pos_s']))
# print(list(datasets['train'][:10]['pos_e']))
# exit(1208)
word_vocab = Vocabulary()
word_vocab.add_word_lst(w_list)
vocabs['word'] = word_vocab
lattice_vocab = Vocabulary()
lattice_vocab.from_dataset(datasets['train'],
field_name='lattice',
no_create_entry_dataset=[
v for k, v in datasets.items()
if k != 'train'
])
vocabs['lattice'] = lattice_vocab
# for k,v in datasets.items():
# v.apply_field(lambda x:[ list(map(lambda x:x[0],p)) for p in x],'skips_l2r','skips_l2r_source')
# v.apply_field(lambda x:[ list(map(lambda x:x[1],p)) for p in x], 'skips_l2r', 'skips_l2r_word')
#
# for k,v in datasets.items():
# v.apply_field(lambda x:[ list(map(lambda x:x[0],p)) for p in x],'skips_r2l','skips_r2l_source')
# v.apply_field(lambda x:[ list(map(lambda x:x[1],p)) for p in x], 'skips_r2l', 'skips_r2l_word')
# for k,v in datasets.items():
# v.apply_field(lambda x:list(map(len,x)), 'skips_l2r_word', 'lexicon_count')
# v.apply_field(lambda x:
# list(map(lambda y:
# list(map(lambda z:word_vocab.to_index(z),y)),x)),
# 'skips_l2r_word',new_field_name='skips_l2r_word')
#
# v.apply_field(lambda x:list(map(len,x)), 'skips_r2l_word', 'lexicon_count_back')
#
# v.apply_field(lambda x:
# list(map(lambda y:
# list(map(lambda z:word_vocab.to_index(z),y)),x)),
# 'skips_r2l_word',new_field_name='skips_r2l_word')
if word_embedding_path is not None:
word_embedding = StaticEmbedding(word_vocab,
word_embedding_path,
word_dropout=0)
embeddings['word'] = word_embedding
if word_char_mix_embedding_path is not None:
lattice_embedding = StaticEmbedding(
lattice_vocab,
word_char_mix_embedding_path,
word_dropout=0.01,
min_freq=lattice_min_freq,
only_train_min_freq=only_train_min_freq)
embeddings['lattice'] = lattice_embedding
vocabs['char'].index_dataset(*(datasets.values()),
field_name='chars',
new_field_name='chars')
vocabs['bigram'].index_dataset(*(datasets.values()),
field_name='bigrams',
new_field_name='bigrams')
vocabs['label'].index_dataset(*(datasets.values()),
field_name='target',
new_field_name='target')
vocabs['lattice'].index_dataset(*(datasets.values()),
field_name='lattice',
new_field_name='lattice')
vocabs['span_label'].index_dataset(*(datasets.values()),
field_name='span_label',
new_field_name='span_label')
vocabs['attr_label'].index_dataset(*(datasets.values()),
field_name='attr_start_label',
new_field_name='attr_start_label')
vocabs['attr_label'].index_dataset(*(datasets.values()),
field_name='attr_end_label',
new_field_name='attr_end_label')
return datasets, vocabs, embeddings
def equip_chinese_ner_with_lexicon(
datasets,
vocabs,
embeddings,
w_list,
word_embedding_path=None,
only_lexicon_in_train=False,
word_char_mix_embedding_path=None, # 字和词的embedding信息
number_normalized=False,
lattice_min_freq=1,
only_train_min_freq=0):
from fastNLP.core import Vocabulary
def normalize_char(inp):
result = []
for c in inp:
if c.isdigit():
result.append('0')
else:
result.append(c)
return result
def normalize_bigram(inp):
result = []
for bi in inp:
tmp = bi
if tmp[0].isdigit():
tmp = '0' + tmp[:1]
if tmp[1].isdigit():
tmp = tmp[0] + '0'
result.append(tmp)
return result
if number_normalized == 3:
for k, v in datasets.items():
v.apply_field(normalize_char, 'chars', 'chars')
vocabs['char'] = Vocabulary()
vocabs['char'].from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
for k, v in datasets.items():
v.apply_field(normalize_bigram, 'bigrams', 'bigrams')
vocabs['bigram'] = Vocabulary()
vocabs['bigram'].from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if only_lexicon_in_train:
print('已支持只加载在trian中出现过的词汇')
def get_skip_path(chars, w_trie):
sentence = ''.join(chars)
result = w_trie.get_lexicon(sentence)
# print(result)
return result
from V0.utils_ import Trie
from functools import partial
from fastNLP.core import Vocabulary
from fastNLP_module import StaticEmbedding
from fastNLP import DataSet
a = DataSet()
w_trie = Trie()
for w in w_list:
w_trie.insert(w)
if only_lexicon_in_train:
lexicon_in_train = set()
for s in datasets['train']['chars']:
lexicon_in_s = w_trie.get_lexicon(s)
for s, e, lexicon in lexicon_in_s:
lexicon_in_train.add(''.join(lexicon))
print('lexicon in train:{}'.format(len(lexicon_in_train)))
print('i.e.: {}'.format(list(lexicon_in_train)[:10]))
w_trie = Trie()
for w in lexicon_in_train:
w_trie.insert(w)
import copy
for k, v in datasets.items():
v.apply_field(partial(get_skip_path, w_trie=w_trie), 'chars',
'lexicons')
v.apply_field(copy.copy, 'chars', 'raw_chars')
v.add_seq_len('lexicons', 'lex_num')
v.apply_field(lambda x: list(map(lambda y: y[0], x)), 'lexicons',
'lex_s')
v.apply_field(lambda x: list(map(lambda y: y[1], x)), 'lexicons',
'lex_e')
if number_normalized == 1:
for k, v in datasets.items():
v.apply_field(normalize_char, 'chars', 'chars')
vocabs['char'] = Vocabulary()
vocabs['char'].from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
if number_normalized == 2:
for k, v in datasets.items():
v.apply_field(normalize_char, 'chars', 'chars')
vocabs['char'] = Vocabulary()
vocabs['char'].from_dataset(
datasets['train'],
field_name='chars',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
for k, v in datasets.items():
v.apply_field(normalize_bigram, 'bigrams', 'bigrams')
vocabs['bigram'] = Vocabulary()
vocabs['bigram'].from_dataset(
datasets['train'],
field_name='bigrams',
no_create_entry_dataset=[datasets['dev'], datasets['test']])
def concat(ins):
chars = ins['chars']
lexicons = ins['lexicons']
result = chars + list(map(lambda x: x[2], lexicons))
# print('lexicons:{}'.format(lexicons))
# print('lex_only:{}'.format(list(filter(lambda x:x[2],lexicons))))
# print('result:{}'.format(result))
return result
def get_pos_s(ins):
lex_s = ins['lex_s']
seq_len = ins['seq_len']
pos_s = list(range(seq_len)) + lex_s
return pos_s
def get_pos_e(ins):
lex_e = ins['lex_e']
seq_len = ins['seq_len']
pos_e = list(range(seq_len)) + lex_e
return pos_e
for k, v in datasets.items():
v.apply(concat, new_field_name='lattice')
v.set_input('lattice')
v.apply(get_pos_s, new_field_name='pos_s')
v.apply(get_pos_e, new_field_name='pos_e')
v.set_input('pos_s', 'pos_e')
word_vocab = Vocabulary()
word_vocab.add_word_lst(w_list)
vocabs['word'] = word_vocab
lattice_vocab = Vocabulary()
lattice_vocab.from_dataset(datasets['train'],
field_name='lattice',
no_create_entry_dataset=[
v for k, v in datasets.items()
if k != 'train'
])
vocabs['lattice'] = lattice_vocab
"""
1.word_embedding_path 这个参数到底是用做什么的？
我将其设置成了 None。但是如果为None，那么embedding['word']没有了还可以吗？
2.StaticEmbedding：
给定预训练embedding的名称或路径，根据vocab从embedding中抽取相应的数据(只会将出现在vocab中的词抽取出来， 如果没有找到，则会随机初始化一个值(但如果该word是被标记为no_create_entry的话，则不会单独创建一个值，而是会被指向unk的index))
"""
if word_embedding_path is not None:
word_embedding = StaticEmbedding(word_vocab,
word_embedding_path,
word_dropout=0)
embeddings['word'] = word_embedding
if word_char_mix_embedding_path is not None:
lattice_embedding = StaticEmbedding(
lattice_vocab,
word_char_mix_embedding_path,
word_dropout=0.01,
min_freq=lattice_min_freq,
only_train_min_freq=only_train_min_freq)
embeddings['lattice'] = lattice_embedding
vocabs['char'].index_dataset(*(datasets.values()),
field_name='chars',
new_field_name='chars')
vocabs['bigram'].index_dataset(*(datasets.values()),
field_name='bigrams',
new_field_name='bigrams')
vocabs['label'].index_dataset(*(datasets.values()),
field_name='target',
new_field_name='target')
vocabs['lattice'].index_dataset(*(datasets.values()),
field_name='lattice',
new_field_name='lattice')
return datasets, vocabs, embeddings