def prepare_dataset_ner(sentences,
char_to_id_ner,
tag_to_id_ner,
lower=False,
train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id_ner["O"]
num = 0
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
num += 1
string = [w[0] for w in s]
chars = [
char_to_id_ner[f(w) if f(w) in char_to_id_ner else '<UNK>']
for w in string
]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id_ner[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
#string： 一个句子
string = [w[0] for w in s]
#chars:获得句子中的每个字 在字典中对应的位置 的列表
chars = [char_to_id[f(w) if f(w) in char_to_id else '<UNK>']
for w in string]
#segs: 使用jieba分词 获取句子中的特征；0:一个字,1、3表示 1到3之间有几个字
segs = get_seg_features("".join(string))
# tags:获得句子中的每个字对应的目标值 在字典中对应的位置 的列表
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- char list of sentences
- char indexes
- segment tag indexes
- tag indexes(if use iobes tagset, I-XXX will be replaced by E-XXX)
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
chars = [char_to_id[f(w) if f(w) in char_to_id else '<UNK>']
for w in string]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append({
"string": string,
"chars": chars,
"segs": segs,
"tags": tags
})
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- [0]text: cut by char
- [1]char indexes
- [2]word length
- [3]tag indexes:
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
#print(sentences)
string = [w[0] for w in s]
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, train=True):
none_index = tag_to_id['O']
data = []
for s in sentences:
string, entity_types, entity_subtype, tags, dep_rels, dep_word_idx = list(
), list(), list(), list(), list(), list()
for w in s:
if w[0] != "...":
string.append(w[0]) # token --> sentence
entity_types.append(w[2])
entity_subtype.append(w[3])
tags.append(w[4])
dep_rels.append(w[5])
dep_word_idx.append(w[-1])
if len(string) > 4:
chars = [
char_to_id[w if w in char_to_id else '<UNK>'] for w in string
]
types = get_seg_features(string, entity_types) # convert to id
subtypes = get_sub_features(string,
entity_subtype) # convert to id
dep_rel_features = get_dep_features(string, dep_rels)
if train:
tags = [tag_to_id[w] for w in tags]
else:
tags = [none_index for _ in chars]
data.append([
string, chars, types, subtypes, tags, dep_rel_features,
dep_word_idx
])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
chars = [char_to_id[f(w) if f(w) in char_to_id else '<UNK>']
for w in string]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
准备用于训练的dataset。
:return:list of lists of dictionaries,包括：词id，字(char)id，标记(tag)id。
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
# print(sentences)
string = [w[0] for w in s]
chars = [char_to_id[f(w) if f(w) in char_to_id else '<UNK>']
for w in string]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s] # Origin word list
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
] # list contains each lowcase word in sentence
segs = get_seg_features(
"".join(string)) # list contains word groups cut by jieba
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, pos_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
chars = [char_to_id[f(w) if f(w) in char_to_id else '<UNK>']
for w in string]
segs = get_seg_features("".join(string))
# pos = get_pos_features("".join(string))
if train:
# print(s)
poss = [pos_to_id[w[-2] if w[-2] in pos_to_id else '<UNK>'] for w in s]
tags = [tag_to_id[w[-1]] for w in s]
else:
poss = [pos_to_id[w[-2] if w[-2] in pos_to_id else '<UNK>'] for w in s]
tags = [none_index for _ in chars]
data.append([string, chars, segs, poss, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, test=False):
"""
把文本型的样本和标签，转化为index，便于输入模型
需要在每个样本和标签前后加<start>和<end>,
但由于pytorch-crf这个包里面会自动添加<start>和<end>的转移概率，
所以我们不用在手动加入。
"""
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
chars = [w[0] for w in s]
tags = [w[-1] for w in s]
""" 句子转化为index """
chars_idx = [
char_to_id[f(c) if f(c) in char_to_id else '<unk>'] for c in chars
]
""" 对句子分词，构造词的长度特征 """
segs_idx = get_seg_features("".join(chars))
if not test:
tags_idx = [tag_to_id[t] for t in tags]
else:
tags_idx = [tag_to_id["<pad>"] for _ in tags]
assert len(chars_idx) == len(segs_idx) == len(tags_idx)
data.append([chars, chars_idx, segs_idx, tags_idx])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, train=True):
none_index = tag_to_id['O']
data = []
import json
doc_file = "doc_dict.utf8"
with codecs.open(doc_file, 'r', 'utf-8') as f:
data_doc = f.readlines()
doc_dict = json.loads(data_doc[0])
for s in sentences:
string, doc_id, entity_types, entity_subtype, tags = list(), list(
), list(), list(), list()
for w in s:
if w[0] != "...":
string.append(w[0])
doc_id.append(w[1])
entity_types.append(w[-3])
entity_subtype.append(w[-2])
tags.append(w[-1])
if len(string) > 4:
chars = [
char_to_id[w if w in char_to_id else '<UNK>'] for w in string
]
doc_chars = get_doc_features(doc_id, char_to_id, doc_dict, chars)
types = get_seg_features(string, entity_types)
subtypes = get_sub_features(string, entity_subtype)
if train:
tags = [tag_to_id[w] for w in tags]
else:
tags = [none_index for _ in chars]
data.append([string, doc_chars, chars, types, subtypes, tags])
return data
def prepare_dataset_(sentences, char_to_id, tag_to_id, train=True):
none_index = tag_to_id['O']
data = []
import json
doc_file = "doc.utf8"
with codecs.open(doc_file, 'r', 'utf-8') as f:
data_doc = f.readlines()
doc_dict = json.loads(data_doc[0])
for s in sentences:
string = [w[0] for w in s]
doc_id = [w[1] for w in s]
chars = [char_to_id[w if w in char_to_id else '<UNK>'] for w in string]
doc_chars = get_doc_features(doc_id, char_to_id, doc_dict, string)
entity_tags = [w[-3] for w in s]
segs = get_seg_features(string, entity_tags)
entity_subtype = [w[-2] for w in s]
subtypes = get_sub_features(string, entity_subtype)
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, doc_chars, chars, segs, subtypes, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
#变成可训练的数据模式
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
#print(sentences)
string = [w[0] for w in s]
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs,
tags]) #string,句子 chars字的id, segs分词id, tags标签id
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
格式化输入到网络中数据
:param sentences:
:param char_to_id:
:param tag_to_id:
:param lower:
:param train:
:return:
"""
none_index = tag_to_id["O"]
# 英文小写转化函数
def to_lower(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
# 这里根据vocab转化ID，没有就用unk填充
chars = [
char_to_id[to_lower(w) if to_lower(w) in char_to_id else '<UNK>']
for w in string
]
# 这个位置提取分词特征，这传入的是之前一个一个词连接好的一句话
segs = get_seg_features("".join(string))
# 如果是训练就需要将标签转化
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
# 这里添加这4个输入
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s] #句子 list
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
] #句子的idx list
segs = get_seg_features("".join(string)) #分词特征list
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
# string:原始的句子的list
# chars:句子中的char对应的id
# segs:将句子进行分词后，每个char的标记
# tags:NER tag的标签的id
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes 每个单词在大字典中出现时的对应id
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences: # 对于训练集list中的每个已划分好的sentence句子
string = [w[0] for w in s] # string是sentence中除去tag的纯word，word用逗号分隔
# ['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
# segs = get_seg_features("".join(string)) # 调用data_utils.py中的get_seg_features函数，利用jieba划分词，生成代表分词后每个词的长度特征
segs = get_seg_features(
" ".join(string)) # 英文不能直接去除空格合并成一句话，还要保留空格以便之后取词
# segs:[1, 3, 1, 3, 0, 0, 1, 3, 1, 3, 0, 0, 1, 3, 1, 3, 0, 1, 3, 0, 1, 2, 3, 0, 1, 2, 2, 3, 0, 0, 1, 3, 1, 3, 0, 0, 1, 2, 2, 3, 0]
if train:
tags = [tag_to_id[w[-1]] for w in s] # tags是训练集标签出现的frequency
else:
tags = [none_index for _ in chars]
data.append([
string, chars, segs, tags
]) # output：训练集word字符——word的frequency——每句话分词后的word特征——标签的frequency
return data
def prepare_dataset(sentences, word_to_id, tag_to_id, train=True):
"""
数据预处理，返回list其实包含
-word_list
-word_id_list
-word char indexs
-tag_id_list
:param sentences:
:param word_to_id:
:param tag_to_id:
:param train:
:return:
"""
none_index = tag_to_id['O']
data = []
for s in sentences:
word_list = [w[0] for w in s]
word_id_list = [
word_to_id[w if w in word_to_id else '<UNK>'] for w in word_list
]
segs = data_utils.get_seg_features("".join(word_list))
if train:
tag_id_list = [tag_to_id[w[-1]] for w in s]
else:
tag_id_list = [none_index for w in s]
data.append([word_list, word_id_list, segs, tag_id_list])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Return:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
基于各映射字典对训练集和验证集的语句序列进行处理，得到将要输入模型的特征列表以及真实标签列表
:param sentences:
:param char_to_id:
:param tag_to_id:
:param lower:
:param train:决定对训练集还是测试集进行处理，默认测试集没有标签，所以全部标注为0
:return:
"""
none_index = tag_to_id["O"]
# print("none_index:", none_index)
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
# print(string)
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
# print("tags", tags)
else:
tags = [none_index for _ in chars]
# print("chars:", chars)
data.append([string, chars, segs, tags])
# print(segs)
return data
def input_from_line(line, char_to_id):
"""
Take sentence data and return an input for
the training or the evaluation function.
"""
line = data_utils.strQ2B(line)
line = data_utils.replace_html(line)
inputs = []
inputs.append([line])
line.replace(" ", "$")
inputs.append([[
char_to_id[char] if char in char_to_id else char_to_id[UNK]
for char in line
]])
inputs.append([data_utils.get_seg_features(line)])
inputs.append([[]])
return inputs
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
# 返回数据格式是:[[[]]] 三个标记分别是字符串,字符串对应的ID，经过结巴分词后词语的长度，字对应的标签的ID
# [[['入', '院', '情', '况', '：', '女', '，', '0', '0', '岁', '，', '以', '突', '发', '言',
# '语', '不', '清', '0', '天', '，', '加', '重', '0', '天', '入', '院', '。', '入', '院', '情',
# '况', '：', '患', '者', '以', '腰', '痛', '伴', '双', '下', '肢', '疼', '痛', '半', '年',
# '，', '加', '重', '0', '0', '余', '天', '为', '主', '诉', '入', '院', '。'],
# [3, 4, 11, 9, 14, 24, 5, 2, 2, 25, 5, 8, 30, 23, 34, 36, 16, 28, 2, 6, 5,
# 10, 13, 2, 6, 3, 4, 7, 3, 4, 11, 9, 14, 27, 31, 8, 33, 12, 19, 22, 15, 32, 29,
# 12, 21, 26, 5, 10, 13, 2, 2, 20, 6, 17, 18, 35, 3, 4, 7],
# [1, 3, 1, 3, 0, 0, 0, 1, 3, 0, 0, 0, 1, 3, 1, 3, 1, 3, 0, 0, 0, 1, 3, 0, 0, 1,
# 3, 0, 1, 3, 1, 3, 0, 1, 3, 0, 1, 3, 0, 1, 2, 3, 1, 3, 1, 3, 0, 1, 3, 1, 3, 1, 3,
# 1, 3, 0, 1, 3, 0],
# [0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 5, 5, 2, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 6, 8, 7, 1, 2, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]
# 这里将数据进行拼接返回，分别包含了word index（在字典中的序列数）word char index（单独按照数字的排列） 字符tag对应的标签的ID
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
# print(sentences)
string = [w[0] for w in s]
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
# 这里获取单词长度的标签，将一个单词标记为122223的格式，其中1是开始字符，2是中间字，3是结束，如果是单个字则直接标记为0
#
# 用结巴分词对单词进行分词，将单词转换歘122223格式例如seg_feature[1, 3, 1, 3, 0, 0, 0]对应的就是两个单词长度为2的和三个字
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
# 这里data的数据是string chars segs tags的合集
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
try:
# print("s:{}".format(s))
# "s:[['我', 'O'], ['要', 'O'], ['看', 'O'], ['乌', 'B-SLOC'], ['鲁', 'I-SLOC'], ['木', 'I-SLOC'], ['齐', 'I-SLOC'], ['市', 'I-SLOC'], ['第', 'I-SLOC'], ['四', 'I-SLOC'], ['十', 'I-SLOC'], ['九', 'I-SLOC'], ['中', 'I-SLOC'], ['学', 'I-SLOC'], ['东', 'I-SLOC'], ['门', 'E-SLOC'], ['去', 'O'], ['乌', 'B-ELOC'], ['鲁', 'I-ELOC'], ['木', 'I-ELOC'], ['齐', 'I-ELOC'], ['推', 'I-ELOC'], ['拿', 'I-ELOC'], ['职', 'I-ELOC'], ['业', 'I-ELOC'], ['学', 'I-ELOC'], ['校', 'I-ELOC'], ['南', 'I-ELOC'], ['门', 'E-ELOC'], ['沿', 'O'], ['西', 'B-ROAD'], ['虹', 'I-ROAD'], ['东', 'I-ROAD'], ['路', 'E-ROAD'], ['的', 'O'], ['监', 'B-TYPE'], ['控', 'E-TYPE']]"
string = [w[0] for w in s]
# print("string:{}".format(string))
# "string:['我', '要', '看', '乌', '鲁', '木', '齐', '市', '第', '四', '十', '九', '中', '学', '东', '门', '去', '乌', '鲁', '木', '齐', '推', '拿', '职', '业', '学', '校', '南', '门', '沿', '西', '虹', '东', '路', '的', '监', '控']"
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>']
for w in string
]
# print("chars:{}".format(chars))
# "chars:[15, 53, 26, 52, 54, 48, 51, 58, 72, 108, 74, 173, 42, 46, 32, 5, 44, 52, 54, 48, 51, 526, 525, 197, 100, 46, 85, 31, 5, 87, 39, 782, 32, 43, 6, 62, 61]"
segs = get_seg_features("".join(string))
# print("segs:{}".format(segs))
# segs: [1, 3, 0, 1, 2, 2, 2, 3, 1, 2, 2, 3, 1, 3, 1, 3, 0, 1, 2, 2, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 3, 1, 3,
# 0, 1, 3]
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
# print("string:{}\nchars:{}\nsegs:{}\ntags:{}\n".format(string, chars, segs, tags))
# "string:['我', '要', '看', '乌', '鲁', '木', '齐', '市', '第', '四', '十', '九', '中', '学', '东', '门', '去', '乌', '鲁', '木', '齐', '推', '拿', '职', '业', '学', '校', '南', '门', '沿', '西', '虹', '东', '路', '的', '监', '控']
# chars:[15, 53, 26, 52, 54, 48, 51, 58, 72, 108, 74, 173, 42, 46, 32, 5, 44, 52, 54, 48, 51, 526, 525, 197, 100, 46, 85, 31, 5, 87, 39, 782, 32, 43, 6, 62, 61]
# segs:[1, 3, 0, 1, 2, 2, 2, 3, 1, 2, 2, 3, 1, 3, 1, 3, 0, 1, 2, 2, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 2, 3, 1, 3, 0, 1, 3]
# tags:[1, 1, 1, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 27, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 28, 1, 29, 26, 26, 30, 1, 5, 6]"exit()
data.append([string, chars, segs, tags])
except:
continue
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
segs = get_seg_features("".join(string))
'''
string 储存的是每个句子里的字
chars 储存的是每个句子里的字对应的id，如果字典中不存在这个字，则返回<'UNK'>(感觉是Unkown的意思)对应的id
segs 存储的是用jieba分词分词后，用基于词长度划分的特征的组合，例如[1,3,|0,|0,|1,2,2,3]：
词长 特征（feature）
1 [0]
2 [1,3]
3 [1,2,3]
4 [1,2,2,3]
5 [1,2,2,2,3]
... ...
注："char".join([strlist]), 意识是将一个字符list中的元素用""中对应的字符连接，这里为空，所以直接连接
'''
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
# data 以句子为单位存储[字符，字符id，标签id/chars长度的全是“0”对应标签id的list，标签]
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
Args:
sentences: 传入的句子（字符与对应的tag标记）
char_to_id: 字符与位置的映射关系
tag_to_id: tag标记与位置的映射关系
"""
none_index = tag_to_id["O"]
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
segs = get_seg_features("".join(string))
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
#string 训练数据集的第一个句子
#['无', '长', '期', '0', '0', '0', '年', '0', '0', '月', '0', '0', '日', '出', '院', '记', '录', '患', '者', '姓', '名', '：', '闫', 'X', 'X', '性', '别', '：', '男', '年', '龄', '：', '0', '0', '岁', '入', '院', '日', '期', '：', '0', '0', '0', '0', '年', '0', '0', '月', '0', '0', '日', '0', '0', '时', '0', '0', '分', '出', '院', '日', '期', '：', '0', '0', '0', '0', '年', '0', '0', '月', '0', '0', '日', '0', '0', '时', '0', '0', '分', '共', '住', '院', '0', '0', '天', '。']
#chars 句子中每个字符在字典中的位置
#[6, 297, 109, 2, 2, 2, 34, 2, 2, 54, 2, 2, 50, 29, 11, 138, 204, 40, 37, 205, 182, 4, 1309, 78, 78, 10, 168, 4, 359, 34, 236, 4, 2, 2, 176, 48, 11, 50, 109, 4, 2, 2, 2, 2, 34, 2, 2, 54, 2, 2, 50, 2, 2, 43, 2, 2, 30, 29, 11, 50, 109, 4, 2, 2, 2, 2, 34, 2, 2, 54, 2, 2, 50, 2, 2, 43, 2, 2, 30, 338, 71, 11, 2, 2, 122, 5]
#segs jieba分词后句子每个词语的长度 0 表示单个字 1表示词语的开头 2表示词语的中间词 3表示词语的结尾
#[0, 1, 3, 1, 2, 3, 0, 1, 3, 0, 1, 3, 0, 1, 3, 1, 3, 1, 3, 1, 3, 0, 0, 1, 3, 1, 3, 0, 0, 1, 3, 0, 1, 3, 0, 1, 3, 1, 3, 0, 1, 2, 2, 3, 0, 1, 3, 0, 1, 3, 0, 1, 3, 0, 1, 3, 0, 1, 3, 1, 3, 0, 1, 2, 2, 3, 0, 1, 3, 0, 1, 3, 0, 1, 3, 0, 1, 3, 1, 3, 1, 3, 1, 3, 0, 0]
#tags 句子中对应的tag标记在字典（tag_to_id）中的位置
#[0, 43, 44, 0, 0, 0, 0, 0, 0, 0, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 16, 0, 0, 0, 0, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
#sentences ��������
#char_to_id ��id
#tag_to_id ��עid
none_index = tag_to_id["O"] #0
def f(x):
return x.lower() if lower else x
data = []
for s in sentences: #s��һ������ [['��', 'O'], ['��', 'B-Dur'], ['��', 'E-Dur'], ['0', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['0', 'B-DRU'], ['0', 'E-DRU'], ['��', 'O'], ['��', 'O'], ['Ժ', 'O'], ['��', 'O'], ['¼', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['X', 'O'], ['X', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['��', 'O'], ['Ժ', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['ʱ', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['��', 'O'], ['Ժ', 'O'], ['��', 'O'], ['��', 'O'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['0', 'B-DRU'], ['0', 'E-DRU'], ['��', 'O'], ['0', 'O'], ['0', 'O'], ['ʱ', 'O'], ['0', 'B-DRU'], ['0', 'E-DRU'], ['��', 'O'], ['��', 'O'], ['ס', 'O'], ['Ժ', 'O'], ['0', 'O'], ['0', 'O'], ['��', 'O'], ['��', 'O']]
#print(sentences)
string = [w[0] for w in s]
#string ['��', '��', '��', '0', '0', '0', '��', '0', '0', '��', '0', '0', '��', '��', 'Ժ', '��', '¼', '��', '��', '��', '��', '��', '��', 'X', 'X', '��', '��', '��', '��', '��', '��', '��', '0', '0', '��', '��', 'Ժ', '��', '��', '��', '0', '0', '0', '0', '��', '0', '0', '��', '0', '0', '��', '0', '0', 'ʱ', '0', '0', '��', '��', 'Ժ', '��', '��', '��', '0', '0', '0', '0', '��', '0', '0', '��', '0', '0', '��', '0', '0', 'ʱ', '0', '0', '��', '��', 'ס', 'Ժ', '0', '0', '��', '��']
# char_to_id �̶�{'<PAD>': 0, '<UNK>': 1, '0': 2, '��': 3, '��': 4, '��': 5, '��': 6, '��': 7, '��': 8, '��': 9, '��': 10, '��': 11, 'Ժ': 12, '��': 13, '��': 14, 'ʷ': 15, 'Ѫ': 16, '��': 17, '��': 18, '˫': 19, 'δ': 20, 'm': 21, '��': 22, '��': 23, '��': 24, '��': 25, '��': 26, '.': 27, '��': 28, '��': 29, '��': 30, 'I': 31, '֫': 32, '��': 33, '��': 34, '��': 35, '��': 36, '��': 37, '/': 38, '��': 39, 'ʹ': 40, '��': 41, '��': 42, 'ʱ': 43, '��': 44, '��': 45, '��': 46, '��': 47, '��': 48, '��': 49, '��': 50, '��': 51, '��': 52, '��': 53, '��': 54, '��': 55, '��': 56, 'ѹ': 57, '��': 58, '��': 59, '��': 60, '��': 61, '��': 62, '��': 63, 'Ĥ': 64, ':': 65, '��': 66, '��': 67, 'ס': 68, '��': 69, '��': 70, 'ҽ': 71, 'ͷ': 72, '��': 73, '��': 74, 'ϵ': 75, '��': 76, 'g': 77, 'X': 78, '��': 79, '��': 80, '��': 81, '��': 82, '-': 83, '��': 84, '��': 85, '��': 86, '��': 87, '��': 88, '��': 89, 'ǰ': 90, '֢': 91, '��': 92, '��': 93, 'V': 94, '��': 95, '��': 96, ',': 97, '��': 98, '��': 99, 'һ': 100, '��': 101, '��': 102, '״': 103, '��': 104, '��': 105, '��': 106, '��': 107, '��': 108, '��': 109, '��': 110, '��': 111, '��': 112, '��': 113, '��': 114, 'С': 115, '��': 116, '��': 117, '��': 118, '��': 119, '��': 120, 'ʾ': 121, '��': 122, '��': 123, '��': 124, '��': 125, '��': 126, 'ͻ': 127, '��': 128, '': 129, 'T': 130, '��': 131, '��': 132, '��': 133, '��': 134, '��': 135, '��': 136, '��': 137, '��': 138, '��': 139, '��': 140, 'ƽ': 141, '��': 142, '��': 143, '��': 144, 'ˮ': 145, '��': 146, '��': 147, 'e': 148, 'ʦ': 149, '��': 150, '��': 151, '��': 152, 'H': 153, 'λ': 154, '��': 155, '��': 156, '��': 157, '��': 158, '': 159, '��': 160, '��': 161, '��': 162, '��': 163, '��': 164, '��': 165, 'ֱ': 166, '��': 167, '��': 168, '��': 169, 'R': 170, 'Ϊ': 171, 'Ƭ': 172, '��': 173, '��': 174, '��': 175, '��': 176, 'P': 177, 'L': 178, '��': 179, '��': 180, '��': 181, '��': 182, '��': 183, 'ҩ': 184, 'Ƥ': 185, 'Ⱦ': 186, '��': 187, '��': 188, '��': 189, '��': 190, '־': 191, '��': 192, '��': 193, '��': 194, '��': 195, 'ͳ': 196, '��': 197, 'ʳ': 198, '��': 199, '��': 200, '��': 201, 'r': 202, '��': 203, '��': 204, '¼': 205, '��': 206, '��': 207, '��': 208, '��': 209, '��': 210, '��': 211, '��': 212, '��': 213, '��': 214, '��': 215, '��': 216, '��': 217, '��': 218, 'ÿ': 219, '��': 220, 'ת': 221, 'B': 222, '��': 223, '��': 224, '��': 225, '��': 226, 'o': 227, '��': 228, '��': 229, 'C': 230, '��': 231, '��': 232, 'i': 233, '��': 234, 'n': 235, '��': 236, '��': 237, '��': 238, 'dz': 239, '��': 240, '��': 241, '��': 242, '��': 243, 'ʪ': 244, '?': 245, '��': 246, '��': 247, 'Ů': 248, 'ƫ': 249, '��': 250, '��': 251, '��': 252, '��': 253, '��': 254, '��': 255, '��': 256, '��': 257, '��': 258, 'l': 259, 'ߵ': 260, '��': 261, '��': 262, '��': 263, '��': 264, '��': 265, '��': 266, '��': 267, '��': 268, '��': 269, '��': 270, '��': 271, '��': 272, '��': 273, '��': 274, '��': 275, '��': 276, '��': 277, '��': 278, 'Ӧ': 279, '��': 280, '��': 281, '֬': 282, '��': 283, '��': 284, 'Σ': 285, ')': 286, '��': 287, 'ȱ': 288, '��': 289, '��': 290, '��': 291, '��': 292, '(': 293, '��': 294, '��': 295, '��': 296, '��': 297, '': 298, '��': 299, '��': 300, '��': 301, '��': 302, '��': 303, '��': 304, '��': 305, 'ϥ': 306, '��': 307, '��': 308, '��': 309, '��': 310, '��': 311, '��': 312, 'ʧ': 313, '��': 314, 'б': 315, '��': 316, '��': 317, '��': 318, '��': 319, '��': 320, '��': 321, '��': 322, '��': 323, '��': 324, '��': 325, '��': 326, '��': 327, '��': 328, 'Ƣ': 329, '��': 330, '��': 331, '��': 332, '��': 333, '��': 334, '��': 335, '��': 336, '��': 337, '��': 338, '��': 339, 'ȷ': 340, '��': 341, 'Բ': 342, '��': 343, '��': 344, 'Ե': 345, '��': 346, '��': 347, '��': 348, '��': 349, '��': 350, '̬': 351, '��': 352, '��': 353, '��': 354, 'ǻ': 355, '��': 356, '��': 357, '��': 358, '��': 359, '��': 360, 'ͫ': 361, '��': 362, '��': 363, '��': 364, '��': 365, '��': 366, '֧': 367, '��': 368, 'ȫ': 369, '��': 370, '��': 371, '��': 372, '��': 373, 'ʯ': 374, '��': 375, 'ǿ': 376, 'u': 377, 'S': 378, 'ǩ': 379, '��': 380, '��': 381, '��': 382, '��': 383, '��': 384, '��': 385, 'ʶ': 386, '��': 387, 'b': 388, '��': 389, '��': 390, '��': 391, '��': 392, '��': 393, 's': 394, '��': 395, '��': 396, '��': 397, '��': 398, '��': 399, '��': 400, '��': 401, '��': 402, '��': 403, 'M': 404, 'A': 405, 'խ': 406, '�': 407, '˯': 408, 'Ӫ': 409, '��': 410, '��': 411, 'Ϣ': 412, '��': 413, '��': 414, '��': 415, '��': 416, '��': 417, '��': 418, '��': 419, 'ָ': 420, 'ͼ': 421, '��': 422, '��': 423, '��': 424, '��': 425, '��': 426, 'ʵ': 427, '��': 428, '��': 429, '��': 430, '��': 431, '��': 432, '��': 433, '��': 434, '��': 435, '��': 436, '��': 437, '��': 438, '��': 439, '��': 440, '��': 441, '��': 442, '��': 443, 'Լ': 444, '��': 445, 'ԭ': 446, '��': 447, '��': 448, '��': 449, 'Χ': 450, '��': 451, '��': 452, '��': 453, '��': 454, '��': 455, '��': 456, '��': 457, '��': 458, '��': 459, '��': 460, '��': 461, '��': 462, '�': 463, '��': 464, 'ĸ': 465, '��': 466, '��': 467, '��': 468, '��': 469, '��': 470, '��': 471, 'Ż': 472, '��': 473, '��': 474, 'Ǻ': 475, '��': 476, '��': 477, 'Ԥ': 478, 'ҵ': 479, '��': 480, '��': 481, '��': 482, '��': 483, 'ְ': 484, '��': 485, '¡': 486, '��': 487, '��': 488, '��': 489, '�': 490, '��': 491, '��': 492, '��': 493, '��': 494, '��': 495, '��': 496, 'ַ': 497, '��': 498, '��': 499, '��': 500, '��': 501, '��': 502, '��': 503, '��': 504, '��': 505, 'd': 506, '��': 507, '̹': 508, 'ũ': 509, '��': 510, '��': 511, '��': 512, '��': 513, '��': 514, '��': 515, '��': 516, 'Ӱ': 517, '��': 518, '��': 519, '��': 520, '��': 521, 'ר': 522, '��': 523, '��': 524, '��': 525, '��': 526, '��': 527, '��': 528, '��': 529, '��': 530, '��': 531, 'ϸ': 532, 'ֳ': 533, '��': 534, '��': 535, '��': 536, '��': 537, '��': 538, '��': 539, '��': 540, '��': 541, '+': 542, '��': 543, 'ľ': 544, '�': 545, 'ή': 546, '��': 547, '��': 548, 'ӯ': 549, 'ë': 550, '��': 551, '��': 552, '��': 553, '��': 554, '��': 555, '��': 556, '˹': 557, '��': 558, '��': 559, '��': 560, '��': 561, '��': 562, 'Ӳ': 563, '��': 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1701, 'þ': 1702, '��': 1703, '��': 1704, '��': 1705, '��': 1706, 'ֶ': 1707, '��': 1708, 'ϲ': 1709, '��': 1710, '��': 1711, '��': 1712, 'ί': 1713, '��': 1714, '��': 1715, '͢': 1716, '��': 1717, '��': 1718, '��': 1719, 'ʩ': 1720, '��': 1721, '��': 1722, '��': 1723, '��': 1724, '��': 1725, '��': 1726, '��': 1727, '��': 1728, '��': 1729, '��': 1730, '��': 1731, '��': 1732, '֥': 1733, '��': 1734, '��': 1735, '��': 1736, '��': 1737, '��': 1738, '��': 1739, '��': 1740, '��': 1741, '#': 1742, '��': 1743, '��': 1744, '��': 1745, '��': 1746, '��': 1747, '��': 1748, '��': 1749, '��': 1750, 'ե': 1751, '��': 1752, '��': 1753, '��': 1754, '��': 1755, 'Ц': 1756, '��': 1757, '��': 1758, '��': 1759, '��': 1760, '��': 1761, '��': 1762, '��': 1763, ';': 1764, '��': 1765, '��': 1766, '��': 1767, '��': 1768, '��': 1769, '��': 1770, '��': 1771, '��': 1772, '��': 1773, '��': 1774, '��': 1775, '��': 1776, '��': 1777, '�': 1778, '��': 1779, '��': 1780, '��': 1781, '��': 1782, '��': 1783, 'Ш': 1784, '��': 1785, 'DZ': 1786, '��': 1787, '��': 1788, '��': 1789, '��': 1790, '��': 1791, '��': 1792, '��': 1793, '��': 1794, 'ʤ': 1795, '��': 1796, '��': 1797, '��': 1798, 'ѯ': 1799, '��': 1800, '': 1801, '��': 1802, '��': 1803, '��': 1804, 'ӡ': 1805, '��': 1806, '��': 1807, '��': 1808, '��': 1809, '��': 1810, '��': 1811, 'Ť': 1812, '��': 1813, '��': 1814, '��': 1815, '��': 1816, '��': 1817, 'Ϫ': 1818, '̶': 1819, '��': 1820, '�': 1821, '��': 1822, 'ʢ': 1823, '��': 1824, '��': 1825, '�': 1826, '��': 1827, '��': 1828, 'о': 1829, '��': 1830, '': 1831, '��': 1832, '��': 1833, 'Ѹ': 1834, '��': 1835, '��': 1836, '��': 1837, '��': 1838, '��': 1839, 'x': 1840, '��': 1841, '��': 1842, '��': 1843, 'Ȱ': 1844, '��': 1845, '��': 1846, '��': 1847, '��': 1848, '�': 1849, '��': 1850, '��': 1851, '��': 1852, '��': 1853, '��': 1854, '��': 1855, '��': 1856, '��': 1857, '��': 1858, '��': 1859, '��': 1860, '��': 1861, '��': 1862, '��': 1863, '��': 1864, '��': 1865, '��': 1866, '��': 1867, '��': 1868, '��': 1869, 'Ǯ': 1870, '��': 1871, '��': 1872, 'Ѽ': 1873, '>': 1874, '��': 1875, '��': 1876, '��': 1877, '��': 1878, '��': 1879, 'ڣ': 1880, '��': 1881, '��': 1882, '��': 1883, '��': 1884, '��': 1885, '��': 1886, '��': 1887, '��': 1888, '��': 1889, 'м': 1890, '��': 1891, '��': 1892, '��': 1893, '��': 1894, '��': 1895, '��': 1896, '��': 1897, 'ִ': 1898, '��': 1899, 'ĺ': 1900, '��': 1901, '��': 1902, '��': 1903, '��': 1904, '��': 1905, '��': 1906, '��': 1907, '��': 1908, '��': 1909, '��': 1910, '�': 1911, '��': 1912, '��': 1913, '��': 1914, '��': 1915, '��': 1916, 'ݡ': 1917, 'ݸ': 1918, '��': 1919, '��': 1920, 'ò': 1921, '��': 1922, '��': 1923, '��': 1924, 'İ': 1925, 'Τ': 1926, '��': 1927, 'w': 1928, 'է': 1929, 'ʲ': 1930, '��': 1931, 'α': 1932, '��': 1933, '��': 1934, '��': 1935, '��': 1936, 'ȯ': 1937, '�': 1938, '��': 1939, 'ͥ': 1940, '��': 1941, '��': 1942, '��': 1943, 'Ľ': 1944, '��': 1945, '��': 1946, '��': 1947, '̽': 1948, '��': 1949, '��': 1950, '��': 1951, '��': 1952, '��': 1953, '��': 1954, '��': 1955, '��': 1956, 'Ϩ': 1957, '��': 1958, '��': 1959, '��': 1960, '��': 1961, '��': 1962, '�': 1963, '˰': 1964, '��': 1965, '��': 1966, '��': 1967, '��': 1968, '��': 1969, '��': 1970, 'ʴ': 1971, 'װ': 1972, '��': 1973, '��': 1974, '��': 1975, '��': 1976, '��': 1977, '��': 1978, '��': 1979, '¹': 1980, '��': 1981, '��': 1982, 'ͤ': 1983, '��': 1984, '��': 1985, '��': 1986, '��': 1987, 'Ƚ': 1988, 'ʣ': 1989, '��': 1990, '��': 1991, '��': 1992, '�': 1993, '��': 1994, '��': 1995, '��': 1996, 'Ļ': 1997, '��': 1998, '��': 1999, '��': 2000, '��': 2001, '��': 2002, '��': 2003, '��': 2004, '��': 2005, 'Я': 2006, '��': 2007, '��': 2008, 'Ȩ': 2009, '��': 2010, '��': 2011, '��': 2012, '��': 2013, '̿': 2014, 'ú': 2015, '��': 2016, '��': 2017, '��': 2018, 'ʸ': 2019, '��': 2020, '��': 2021, '��': 2022, '��': 2023, '��': 2024, '��': 2025, '��': 2026, '��': 2027, 'Ө': 2028, '��': 2029, '��': 2030, 'Ϻ': 2031, '��': 2032, '��': 2033, '̸': 2034, '��': 2035, '��': 2036, '��': 2037, '��': 2038, '��': 2039, '��': 2040, '��': 2041, '�': 2042, 'ʻ': 2043, '��': 2044, '��': 2045, '��': 2046, '��': 2047, "'": 2048, '<': 2049, 'Ǭ': 2050, '��': 2051, '��': 2052, '��': 2053, '��': 2054, '��': 2055, '��': 2056, '��': 2057, '��': 2058, '��': 2059, '��': 2060, '��': 2061, '��': 2062, '��': 2063, '��': 2064, '��': 2065, '��': 2066, '��': 2067, '��': 2068, 'ǽ': 2069, '��': 2070, '��': 2071, '��': 2072, '��': 2073, '��': 2074, '�': 2075, '��': 2076, '��': 2077, 'ñ': 2078, '��': 2079, 'Ϥ': 2080, '��': 2081, '��': 2082, '��': 2083, '': 2084, '��': 2085, '��': 2086, '�': 2087, '��': 2088, 'ŷ': 2089, '֭': 2090, '��': 2091, '�': 2092, '��': 2093, '��': 2094, '��': 2095, 'Ϳ': 2096, 'ɬ': 2097, '��': 2098, '��': 2099, '��': 2100, '�': 2101, 'ʨ': 2102, 'Ѣ': 2103, '��': 2104, '��': 2105, '��': 2106, '��': 2107, '��': 2108, '��': 2109, '��': 2110, '��': 2111, '��': 2112, 'Ҹ': 2113, '��': 2114, '��': 2115, '��': 2116, '��': 2117, '��': 2118, 'ԣ': 2119, '��': 2120, '֤': 2121, '��': 2122, '��': 2123, '��': 2124, '��': 2125, '��': 2126, 'Ǩ': 2127, '��': 2128, '��': 2129, '۳': 2130, '��': 2131, 'ͭ': 2132, '�': 2133, 'п': 2134, '��': 2135, '��': 2136, 'Ь': 2137, '��': 2138, '��': 2139, '��': 2140, 'Ĭ': 2141, '��': 2142, '��': 2143, '��': 2144, '��': 2145, '��': 2146, '��': 2147, '��': 2148, '��': 2149, '��': 2150, '��': 2151, '��': 2152, '��': 2153, 'ǧ': 2154, '��': 2155, '��': 2156, '��': 2157, '��': 2158, '��': 2159, '��': 2160, '��': 2161, '��': 2162, '��': 2163, '��': 2164, '��': 2165, '��': 2166, '��': 2167, '��': 2168, '��': 2169, '��': 2170, '׳': 2171, 'ز': 2172, '��': 2173, '��': 2174, '��': 2175, '¦': 2176, 'լ': 2177, '�': 2178, '�': 2179, '��': 2180, '��': 2181, '��': 2182, '��': 2183, 'ϯ': 2184, '��': 2185, '��': 2186, '��': 2187, '��': 2188, '��': 2189, '��': 2190, '��': 2191, '�': 2192, 'Ը': 2193, '��': 2194, '��': 2195, '��': 2196, '��': 2197, 'ץ': 2198, 'ȭ': 2199, '˺': 2200, '��': 2201, '��': 2202, '��': 2203, '��': 2204, '��': 2205, '��': 2206, '��': 2207, '��': 2208, 'ɣ': 2209, '�': 2210, '��': 2211, '��': 2212, 'ϴ': 2213, '��': 2214, '��': 2215, '��': 2216, '��': 2217, 'ϩ': 2218, 'ɿ': 2219, 'ү': 2220, 'ˬ': 2221, '��': 2222, '��': 2223, '�': 2224, '��': 2225, 'η': 2226, '��': 2227, '��': 2228, '��': 2229, '˲': 2230, '��': 2231, '��': 2232, '��': 2233, '��': 2234, 'Ʊ': 2235, '��': 2236, '��': 2237, 'Ҥ': 2238, '��': 2239, '��': 2240, '��': 2241, '��': 2242, '��': 2243, '��': 2244, '��': 2245, '��': 2246, '��': 2247, '��': 2248, '��': 2249, '��': 2250, '��': 2251, 'â': 2252, '��': 2253, 'έ': 2254, '��': 2255, '��': 2256, '��': 2257, '��': 2258, '��': 2259, 'ӫ': 2260, '��': 2261, 'ݭ': 2262, '��': 2263, '��': 2264, '��': 2265, '��': 2266, '��': 2267, '�': 2268, '��': 2269, '�': 2270, '��': 2271, '��': 2272, '��': 2273, '��': 2274, '��': 2275, 'ղ': 2276, '��': 2277, '��': 2278, 'á': 2279, '��': 2280, '��': 2281, '��': 2282, '��': 2283, '̤': 2284, '��': 2285, 'ӭ': 2286, '��': 2287, '��': 2288, '��': 2289, '��': 2290, '��': 2291, '��': 2292, '��': 2293, '��': 2294, '��': 2295, '��': 2296, '��': 2297, '��': 2298, '��': 2299, '��': 2300, '��': 2301, '��': 2302, '��': 2303, '��': 2304, '��': 2305, '��': 2306, 'ȧ': 2307, '��': 2308, '��': 2309, '��': 2310, '��': 2311, '&': 2312, '|': 2313, '��': 2314, '��': 2315, '��': 2316, '��': 2317, '��': 2318, '��': 2319, '��': 2320, '��': 2321, '��': 2322, '��': 2323, '��': 2324, '٤': 2325, '��': 2326, '��': 2327, '��': 2328, '��': 2329, '��': 2330, '��': 2331, 'д': 2332, '��': 2333, '��': 2334, '��': 2335, 'ƾ': 2336, '��': 2337, 'ɲ': 2338, '��': 2339, '��': 2340, '��': 2341, '��': 2342, '��': 2343, '��': 2344, '��': 2345, '��': 2346, '��': 2347, '��': 2348, '��': 2349, '̾': 2350, '��': 2351, '��': 2352, '��': 2353, '��': 2354, '��': 2355, '��': 2356, '��': 2357, '��': 2358, 'Ŷ': 2359, '��': 2360, '��': 2361, 'ơ': 2362, '��': 2363, '��': 2364, '��': 2365, '��': 2366, '��': 2367, 'Ȧ': 2368, 'ʥ': 2369, '��': 2370, '��': 2371, '��': 2372, '��': 2373, '��': 2374, '��': 2375, '��': 2376, '��': 2377, '�': 2378, '��': 2379, '�': 2380, 'Ӥ': 2381, 'ɩ': 2382, 'Т': 2383, '��': 2384, '��': 2385, '��': 2386, '��': 2387, 'Ѱ': 2388, '��': 2389, '��': 2390, '�': 2391, '��': 2392, '��': 2393, 'Ρ': 2394, '��': 2395, '��': 2396, 'ϣ': 2397, '��': 2398, '®': 2399, '��': 2400, '��': 2401, '��': 2402, 'æ': 2403, '��': 2404, '��': 2405, '��': 2406, 'ǡ': 2407, '�': 2408, '��': 2409, '��': 2410, '�': 2411, '��': 2412, '��': 2413, 'и': 2414, 'ܲ': 2415, '��': 2416, '��': 2417, '��': 2418, '��': 2419, 'Ͷ': 2420, '��': 2421, '��': 2422, '��': 2423, '��': 2424, '��': 2425, 'ҡ': 2426, 'ײ': 2427, '��': 2428, '��': 2429, '��': 2430, '��': 2431, '��': 2432, '��': 2433, '��': 2434, '��': 2435, '��': 2436, 'ɱ': 2437, '�': 2438, '��': 2439, '��': 2440, '��': 2441, '��': 2442, '��': 2443, '��': 2444, '��': 2445, '��': 2446, '��': 2447, '��': 2448, '��': 2449, '��': 2450, '��': 2451, '��': 2452, '��': 2453, '��': 2454, '��': 2455, 'ع': 2456, '��': 2457, '��': 2458, '��': 2459, '��': 2460, '��': 2461, '��': 2462, 'û': 2463, '��': 2464, '��': 2465, '��': 2466, '��': 2467, 'Ӿ': 2468, '�': 2469, '��': 2470, '��': 2471, '��': 2472, '�': 2473, '��': 2474, '�': 2475, 'μ': 2476, '��': 2477, '��': 2478, '��': 2479, '��': 2480, '��': 2481, '��': 2482, '��': 2483, '��': 2484, '¯': 2485, 'Ȳ': 2486, '��': 2487, '��': 2488, '��': 2489, 'ĵ': 2490, '��': 2491, '��': 2492, '��': 2493, '��': 2494, '��': 2495, '��': 2496, '��': 2497, '��': 2498, '��': 2499, 'ɺ': 2500, '�': 2501, '��': 2502, '��': 2503, 'ƿ': 2504, '߰': 2505, '��': 2506, '��': 2507, '��': 2508, '��': 2509, '��': 2510, '��': 2511, '��': 2512, '��': 2513, '��': 2514, '��': 2515, '�': 2516, '��': 2517, '��': 2518, '��': 2519, '��': 2520, '��': 2521, '��': 2522, '��': 2523, '��': 2524, '��': 2525, '��': 2526, '��': 2527, '��': 2528, '��': 2529, '��': 2530, '��': 2531, '��': 2532, '��': 2533, '�': 2534, '��': 2535, '��': 2536, '��': 2537, '��': 2538, '��': 2539, '��': 2540, '�': 2541, 'в': 2542, '��': 2543, '��': 2544, '��': 2545, '��': 2546, '��': 2547, '��': 2548, '��': 2549, '��': 2550, '��': 2551, '��': 2552, '��': 2553, 'ܿ': 2554, '��': 2555, 'ѿ': 2556, '��': 2557, 'ï': 2558, '��': 2559, '��': 2560, '��': 2561, '��': 2562, '��': 2563, '��': 2564, '��': 2565, '��': 2566, 'ε': 2567, '��': 2568, '��': 2569, '��': 2570, '��': 2571, '��': 2572, '��': 2573, '��': 2574, '��': 2575, '��': 2576, '�': 2577, '��': 2578, '��': 2579, '��': 2580, '��': 2581, 'ԥ': 2582, '��': 2583, '��': 2584, '��': 2585, 'ó': 2586, '��': 2587, '��': 2588, '��': 2589, 'շ': 2590, '��': 2591, '��': 2592, '��': 2593, '��': 2594, '��': 2595, '��': 2596, 'ۨ': 2597, '۪': 2598, '��': 2599, '��': 2600, '��': 2601, '��': 2602, '��': 2603, '��': 2604, 'Ǧ': 2605, '��': 2606, '��': 2607, '�': 2608, '': 2609, '��': 2610, '��': 2611, '��': 2612, '��': 2613, '��': 2614, '��': 2615, '��': 2616, '��': 2617, '��': 2618, '��': 2619, 'Ʈ': 2620, '��': 2621, '��': 2622, '��': 2623, '��': 2624, 'ɧ': 2625, '��': 2626, '��': 2627, '��': 2628, '��': 2629, '��': 2630, '��': 2631, '��': 2632, '��': 2633, '��': 2634, '��': 2635, '��': 2636, '��': 2637, '��': 2638, '��': 2639, '��': 2640, '��': 2641, '��': 2642, '��': 2643, '��': 2644, '��': 2645, '��': 2646, '��': 2647, 'Ԣ': 2648, '�': 2649, '��': 2650, '��': 2651, '��': 2652, '��': 2653, '�': 2654, 'ɼ': 2655, '�': 2656, '��': 2657, '��': 2658, '��': 2659, '��': 2660, '��': 2661, '��': 2662, '��': 2663, '��': 2664, '��': 2665, '��': 2666, '��': 2667, '��': 2668, '��': 2669, 'ޱ': 2670, '��': 2671, '��': 2672, 'ң': 2673, '��': 2674, '��': 2675, '��': 2676}
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
# chars [6, 305, 110, 2, 2, 2, 35, 2, 2, 55, 2, 2, 51, 30, 12, 138, 205, 39, 37, 204, 188, 4, 1287, 78, 78, 10, 167, 4, 365, 35, 240, 4, 2, 2, 175, 48, 12, 51, 110, 4, 2, 2, 2, 2, 35, 2, 2, 55, 2, 2, 51, 2, 2, 43, 2, 2, 29, 30, 12, 51, 110, 4, 2, 2, 2, 2, 35, 2, 2, 55, 2, 2, 51, 2, 2, 43, 2, 2, 29, 343, 68, 12, 2, 2, 122, 5]
segs = get_seg_features("".join(string)) #���Ӱ�sbie�ִʱ�ע
if train:
#tag_to_id �̶�{'O': 0, 'I-TES': 1, 'I-DIS': 2, 'I-SGN': 3, 'B-TES': 4, 'E-TES': 5, 'B-REG': 6, 'E-REG': 7, 'B-SYM': 8, 'E-SYM': 9, 'B-SGN': 10, 'E-SGN': 11, 'I-SYM': 12, 'B-DIS': 13, 'E-DIS': 14, 'B-DRU': 15, 'E-DRU': 16, 'B-ORG': 17, 'E-ORG': 18, 'I-DRU': 19, 'I-PT': 20, 'I-REG': 21, 'I-ORG': 22, 'B-PT': 23, 'E-PT': 24, 'S-DRU': 25, 'B-DEG': 26, 'E-DEG': 27, 'S-REG': 28, 'S-SYM': 29, 'S-DIS': 30, 'B-FW': 31, 'E-FW': 32, 'B-PRE': 33, 'E-PRE': 34, 'I-SUR': 35, 'I-FW': 36, 'I-PRE': 37, 'B-CL': 38, 'E-CL': 39, 'S-ORG': 40, 'B-SUR': 41, 'E-SUR': 42, 'B-Dur': 43, 'E-Dur': 44, 'B-PSB': 45, 'E-PSB': 46, 'I-CL': 47, 'I-DEG': 48, 'I-PSB': 49, 'S-TES': 50}
tags = [tag_to_id[w[-1]] for w in s] #��ע����ʵ��ִ�id
# tags [0, 43, 44, 0, 0, 0, 0, 0, 0, 0, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 16, 0, 0, 0, 0, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0]
else:
tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
#string ['��', 'Ժ', '��', '��', '��', '��', '��', '0', '0', '��', '��', '��', 'ͻ', '��', '��', '��', '֫', '��', '��', '��', '0', '0', 'С', 'ʱ', 'Ϊ', '��', '��', '��', '��', '��', '��', 'Ѫ', 'ѹ', '��', 'ʷ', '0', '0', '��', '��', '��', 'Ѫ', 'ѹ', '��', '��', '0', '0', '0', '/', '��']
#chars [48, 12, 76, 96, 4, 365, 3, 2, 2, 175, 3, 152, 127, 46, 24, 9, 32, 22, 6, 17, 2, 2, 115, 43, 171, 104, 270, 25, 292, 291, 62, 16, 57, 11, 15, 2, 2, 330, 35, 3, 16, 57, 564, 62, 2, 2, 2, 38, 5]
#segs[1, 3, 1, 3, 0, 0, 0, 1, 3, 0, 0, 0, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 0, 0, 1, 3, 1, 2, 3, 1, 3, 1, 3, 1, 3, 0, 1, 3, 1, 3, 1, 2, 3, 0, 0]
#tags [0, 0, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 8, 9, 6, 21, 21, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 23, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0, 0, 0, 0, 0, 0]
return data
def prepare_dataset(sentences,
char_to_id,
tag_to_id,
lower=False,
train=True,
isenglish=False):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
#none_index = tag_to_id["O"]
# for k in tag_to_id:
# print(k)
def f(x):
return x.lower() if lower else x
data = []
for s in sentences:
string = [w[0] for w in s]
#tag = [w[-1] for w in s]
chars = [
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
]
if isenglish:
segs = [0] * len(chars)
else:
segs = get_seg_features("".join(string))
if train:
#print (tag)
tags = [tag_to_id[w[-1]] for w in s]
# else:
# tags = [none_index for _ in chars]
data.append([string, chars, segs, tags])
return data
def prepare_dataset(sentences, char_to_id, tag_to_id, lower=False, test=False):
"""
把文本型的样本和标签，转化为index，便于输入模型
需要在每个样本和标签前后加<start>和<end>
"""
def f(x): return x.lower() if lower else x
data = []
for s in sentences:
chars = [w[0] for w in s]
tags = [w[-1] for w in s]
""" 句子转化为index """
chars_idx = [char_to_id[f(c) if f(c) in char_to_id else '<unk>'] for c in chars]
""" 对句子分词，构造词的长度特征 """
segs_idx = get_seg_features("".join(chars))
""" 每个样本前后加<start>和<end> """
chars_idx = [char_to_id["<start>"]] + chars_idx + [char_to_id["<end>"]]
segs_idx = [0] + segs_idx + [0]
""" 把标签转化为index, 标签前后加<start>和<end> """
tags = ["<start>"] + tags + ["<end>"]
if not test:
tags_idx = [tag_to_id[t] for t in tags]
else:
tags_idx = [tag_to_id["<pad>"] for _ in tags]
assert len(chars_idx) == len(segs_idx) == len(tags_idx)
data.append([chars_idx, segs_idx, tags_idx])
return data
def prepare_dataset(sentences,
char_to_id,
tag_to_id,
mark_to_id,
entropy_dict,
lower=False,
train=True):
"""
Prepare the dataset. Return a list of lists of dictionaries containing:
- word indexes
- word char indexes
- tag indexes
"""
none_index = tag_to_id["O"] # 目前train始终未true，none_index并未作用
def f(x):
return x.lower() if lower else x
data = []
dis_dict_map = {"O": 0, "B": 1, "I": 2, "E": 3, "S": 4}
loc_dict_map = {"O": 0, "B": 1, "I": 2, "E": 3, "S": 4}
res_dict_map = {"O": 0, "B": 1, "I": 2, "E": 3, "S": 4}
for s in sentences:
string = ' '.join([w[0] for w in s])
chars = ' '.join([
char_to_id[f(w) if f(w) in char_to_id else '<UNK>'] for w in string
])
segs = ' '.join(get_seg_features("".join(string)))
marks = ' '.join(
[mark_to_id[w[1] if w[1] in mark_to_id else '<UNK>'] for w in s])
complete_dis_dicts = ' '.join([dis_dict_map[w[1]] for w in s])
partial_dis_dicts = ' '.join([dis_dict_map[w[2]] for w in s])
pinyin_dis_dicts = ' '.join([dis_dict_map[w[3]] for w in s])
complete_loc_dicts = ' '.join([loc_dict_map[w[4]] for w in s])
partial_loc_dicts = ' '.join([loc_dict_map[w[5]] for w in s])
pinyin_loc_dicts = ' '.join([loc_dict_map[w[6]] for w in s])
complete_res_dicts = ' '.join([res_dict_map[w[7]] for w in s])
partial_res_dicts = ' '.join([res_dict_map[w[8]] for w in s])
pinyin_res_dicts = ' '.join([res_dict_map[w[9]] for w in s])
# 左右熵计算(不存在时存储多少有待考虑，目前用均值填充)
left_entropy = [
entropy_dict[w][0] if w in entropy_dict else 0 for w in string
]
# 计算非0的均值可能会出bug：一个query仅有一个字非0，其他均为0时会出现除0；这里用整体均值
# left_entropy_average = np.mean(np.array(left_entropy)[np.array(left_entropy) != 0])
left_entropy_average = np.mean(np.array(left_entropy))
left_entropy = [
elem if elem != 0 else left_entropy_average
for elem in left_entropy
]
if max(left_entropy) != min(left_entropy):
left_entropy = [[(i - min(left_entropy)) /
(max(left_entropy) - min(left_entropy))]
for i in left_entropy]
else:
left_entropy = [[i] for i in left_entropy]
left_entropy = ' '.join(left_entropy)
right_entropy = [
entropy_dict[w][1] if w in entropy_dict else 0 for w in string
]
# right_entropy_average = np.mean(np.array(right_entropy)[np.array(right_entropy) != 0])
right_entropy_average = np.mean(np.array(right_entropy))
right_entropy = [
elem if elem != 0 else right_entropy_average
for elem in right_entropy
]
if max(right_entropy) != min(right_entropy):
right_entropy = [[(i - min(right_entropy)) /
(max(right_entropy) - min(right_entropy))]
for i in right_entropy]
else:
right_entropy = [[i] for i in right_entropy]
right_entropy = ' '.join(right_entropy)
if train:
tags = [tag_to_id[w[-1]] for w in s]
else:
tags = [none_index for _ in chars]
tags = ' '.join(tags)
data.append([
string, chars, segs, marks, complete_dis_dicts, partial_dis_dicts,
pinyin_dis_dicts, complete_loc_dicts, partial_loc_dicts,
pinyin_loc_dicts, complete_res_dicts, partial_res_dicts,
pinyin_res_dicts, left_entropy, right_entropy, tags
])
return data
