def __init__(self):
# 加载数据
self.sents_src, self.sents_tgt = read_corpus(data_path)
self.tokenier = Tokenizer(word2idx)
# 判断是否有可用GPU
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
print("device: " + str(self.device))
# 定义模型
self.bert_model = load_bert(word2idx,
model_name=model_name,
model_class="sequence_labeling",
target_size=len(target))
## 加载预训练的模型参数～
self.bert_model.load_pretrain_params(model_path)
# 将模型发送到计算设备(GPU或CPU)
self.bert_model.set_device(self.device)
# 声明需要优化的参数
self.optim_parameters = list(self.bert_model.parameters())
self.optimizer = torch.optim.Adam(self.optim_parameters,
lr=lr,
weight_decay=1e-3)
# 声明自定义的数据加载器
dataset = NERDataset(self.sents_src, self.sents_tgt)
self.dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=collate_fn)
def ner_print(model, test_data, vocab_path, device="cpu"):
model.eval()
word2idx = load_chinese_base_vocab(vocab_path)
tokenier = Tokenizer(word2idx)
trans = model.state_dict()["crf_layer.trans"]
for text in test_data:
decode = []
text_encode, text_ids = tokenier.encode(text)
text_tensor = torch.tensor(text_encode, device=device).view(1, -1)
out = model(text_tensor).squeeze(0) # 其实是nodes
labels = viterbi_decode(out, trans)
starting = False
for l in labels:
if l > 0:
label = target[l.item()]
decode.append(label)
else :
decode.append("other")
flag = 0
res = {}
for index, each_entity in enumerate(decode):
if each_entity != "other":
if flag != each_entity:
cur_text = text[index - 1]
if each_entity in res.keys():
res[each_entity].append(cur_text)
else :
res[each_entity] = [cur_text]
flag = each_entity
elif flag == each_entity:
res[each_entity][-1] += text[index - 1]
else :
flag = 0
print(res)
def __init__(self, word2ix, model_name="roberta", tokenizer=None):
super(Seq2SeqModel, self).__init__()
self.word2ix = word2ix
if tokenizer is None:
self.tokenizer = Tokenizer(word2ix)
else:
self.tokenizer = tokenizer
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLMPredictionHead
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(
config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLMPredictionHead
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(
config, self.bert.embeddings.word_embeddings.weight)
else:
raise Exception("model_name_err")
self.hidden_dim = config.hidden_size
self.vocab_size = len(word2ix)
def __init__(self, data) :
## 一般init函数是加载所有数据
super(BertDataset, self).__init__()
self.data = data
print("data size is " + str(len(data)))
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
def __init__(self):
# 加载数据
data_path = "./corpus/新闻标题文本分类/Train.txt"
self.vocab_path = "./state_dict/roberta_wwm_vocab.txt" # roberta模型字典的位置
self.sents_src, self.sents_tgt = read_corpus(data_path)
self.model_name = "roberta" # 选择模型名字
self.model_path = "./state_dict/roberta_wwm_pytorch_model.bin" # roberta模型位置
self.recent_model_path = "" # 用于把已经训练好的模型继续训练
self.model_save_path = "./bert_multi_classify_model.bin"
self.batch_size = 16
self.lr = 1e-5
# 加载字典
self.word2idx = load_chinese_base_vocab(self.vocab_path)
self.tokenier = Tokenizer(self.word2idx)
# 判断是否有可用GPU
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device: " + str(self.device))
# 定义模型
self.bert_model = load_bert(self.vocab_path, model_name=self.model_name, model_class="encoder", target_size=len(target))
## 加载预训练的模型参数～
load_model_params(self.bert_model, self.model_path)
# 将模型发送到计算设备(GPU或CPU)
self.bert_model.to(self.device)
# 声明需要优化的参数
self.optim_parameters = list(self.bert_model.parameters())
self.optimizer = torch.optim.Adam(self.optim_parameters, lr=self.lr, weight_decay=1e-3)
# 声明自定义的数据加载器
dataset = NLUDataset(self.sents_src, self.sents_tgt, self.vocab_path)
self.dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, collate_fn=collate_fn)
def __init__(self):
## 一般init函数是加载所有数据
super(BertDataset, self).__init__()
## 拿到所有文件名字
self.txts = glob.glob('./state_dict/THUCNews/*/*.txt')
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
def __init__(self, data):
## 一般init函数是加载所有数据
super(ExtractDataset, self).__init__()
# 读原始数据
# self.sents_src, self.sents_tgt = read_corpus(poem_corpus_dir)
self.data = data
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
def read_corpus(dir_path, vocab_path):
"""
读原始数据
"""
sents_src = []
sents_tgt = []
word2idx = load_chinese_base_vocab(vocab_path, simplfied=True)
tokenizer = Tokenizer(word2idx)
files = os.listdir(dir_path) #得到文件夹下的所有文件名称
for file1 in files: #遍历文件夹
if not os.path.isdir(file1): #判断是否是文件夹，不是文件夹才打开
file_path = dir_path + "/" + file1
print(file_path)
if file_path[-3:] != "csv":
continue
df = pd.read_csv(file_path)
# 先判断诗句的类型 再确定是否要构造数据
for index, row in df.iterrows():
if type(row[0]) is not str or type(row[3]) is not str:
continue
if len(row[0].split(" ")) > 1:
# 说明题目里面存在空格，只要空格前面的数据
row[0] = row[0].split(" ")[0]
if len(row[0]) > 10 or len(row[0]) < 1:
# 过滤掉题目长度过长和过短的诗句
continue
encode_text = tokenizer.encode(row[3])[0]
if word2idx["[UNK]"] in encode_text:
# 过滤unk字符
continue
if len(row[3]) == 24 and (row[3][5] == "，"
or row[3][5] == "。"):
# 五言绝句
sents_src.append(row[0] + "##" + "五言绝句")
sents_tgt.append(row[3])
elif len(row[3]) == 32 and (row[3][7] == "，"
or row[3][7] == "。"):
# 七言绝句
sents_src.append(row[0] + "##" + "七言绝句")
sents_tgt.append(row[3])
elif len(row[3]) == 48 and (row[3][5] == "，"
or row[3][5] == "。"):
# 五言律诗
sents_src.append(row[0] + "##" + "五言律诗")
sents_tgt.append(row[3])
elif len(row[3]) == 64 and (row[3][7] == "，"
or row[3][7] == "。"):
# 七言律诗
sents_src.append(row[0] + "##" + "七言律诗")
sents_tgt.append(row[3])
print("第一个诗句数据集共: " + str(len(sents_src)) + "篇")
return sents_src, sents_tgt
def __init__(self, data, vocab_path) :
## 一般init函数是加载所有数据
super(ExtractDataset, self).__init__()
# 读原始数据
# self.sents_src, self.sents_tgt = read_corpus(poem_corpus_dir)
self.data = data
self.word2idx = load_chinese_base_vocab(vocab_path)
self.idx2word = {k: v for v, k in self.word2idx.items()}
self.tokenizer = Tokenizer(self.word2idx)
def __init__(self, word2ix, tokenizer=None):
super().__init__()
self.word2ix = word2ix
if tokenizer is not None:
self.tokenizer = tokenizer
else:
self.tokenizer = Tokenizer(word2ix)
self.config = GPT2Config(len(word2ix))
self.model = GPT2LMHeadModel(self.config)
def __init__(self, sents_src, sents_tgt, vocab_path):
## 一般init函数是加载所有数据
super(BertDataset, self).__init__()
# 读原始数据
# self.sents_src, self.sents_tgt = read_corpus(poem_corpus_dir)
self.sents_src = sents_src
self.sents_tgt = sents_tgt
self.word2idx = load_chinese_base_vocab(vocab_path, simplfied=True)
self.idx2word = {k: v for v, k in self.word2idx.items()}
self.tokenizer = Tokenizer(self.word2idx)
def __init__(self, word2ix, model_name="roberta", tokenizer=None):
super(Seq2SeqModel, self).__init__(word2ix=word2ix, model_name=model_name)
self.word2ix = word2ix
if tokenizer is None:
self.tokenizer = Tokenizer(word2ix)
else:
self.tokenizer = tokenizer
self.hidden_dim = self.config.hidden_size
self.vocab_size = len(word2ix)
def read_corpus_2(dir_path, vocab_path):
"""读取最近的一个数据集 唐诗和宋诗 """
sents_src = []
sents_tgt = []
word2idx = load_chinese_base_vocab(vocab_path, simplfied=True)
tokenizer = Tokenizer(word2idx)
files = os.listdir(dir_path) #得到文件夹下的所有文件名称
for file1 in files: #遍历文件夹
if not os.path.isdir(file1): #判断是否是文件夹，不是文件夹才打开
file_path = dir_path + "/" + file1
print(file_path)
# data = json.load(file_path)
with open(file_path) as f:
poem_list = eval(f.read())
for each_poem in poem_list:
string_list = each_poem["paragraphs"]
poem = ""
for each_s in string_list:
poem += each_s
cc = opencc.OpenCC('t2s')
poem = cc.convert(poem)
encode_text = tokenizer.encode(poem)[0]
if word2idx["[UNK]"] in encode_text:
# 过滤unk字符
continue
title = cc.convert(each_poem["title"])
if len(title) > 10 or len(title) < 1:
# 过滤掉题目长度过长和过短的诗句
continue
if len(poem) == 24 and (poem[5] == "，" or poem[5] == "。"):
# 五言绝句
sents_src.append(title + "##" + "五言绝句")
sents_tgt.append(poem)
elif len(poem) == 32 and (poem[7] == "，" or poem[7] == "。"):
# 七言绝句
sents_src.append(title + "##" + "七言绝句")
sents_tgt.append(poem)
elif len(poem) == 48 and (poem[5] == "，" or poem[5] == "。"):
# 五言律诗
sents_src.append(title + "##" + "五言律诗")
sents_tgt.append(poem)
elif len(poem) == 64 and (poem[7] == "，" or poem[7] == "。"):
# 七言律诗
sents_src.append(title + "##" + "七言律诗")
sents_tgt.append(poem)
print("第二个诗句数据集共:" + str(len(sents_src)) + "篇")
return sents_src, sents_tgt
def read_corpus_ci(dir_path, vocab_path):
""" 读取宋词数据集"""
import json, sys
import sqlite3
from collections import OrderedDict
word2idx = load_chinese_base_vocab(vocab_path, simplfied=True)
tokenizer = Tokenizer(word2idx)
try: # Python 2
reload(sys)
sys.setdefaultencoding('utf-8')
except NameError: # Python 3
pass
c = sqlite3.connect(dir_path + '/ci.db')
cursor = c.execute("SELECT name, long_desc, short_desc from ciauthor;")
d = {"name": None, "description": None, "short_description": None}
cursor = c.execute("SELECT rhythmic, author, content from ci;")
d = {"rhythmic": None, "author": None, "paragraphs": None}
# cis = []
sents_src = []
sents_tgt = []
for row in cursor:
ci = OrderedDict(sorted(d.items(), key=lambda t: t[0]))
ci["rhythmic"] = row[0]
ci["author"] = row[1]
ci["paragraphs"] = row[2].split('\n')
string = ""
for s in ci["paragraphs"]:
if s == " >> " or s == "词牌介绍":
continue
string += s
encode_text = tokenizer.encode(string)[0]
if word2idx["[UNK]"] in encode_text:
# 过滤unk字符
continue
sents_src.append(row[0] + "##词")
sents_tgt.append(string)
# cis.append(ci)
# print(cis[:10])
print("词共: " + str(len(sents_src)) + "篇")
return sents_src, sents_tgt
def __init__(self):
super(BertDataset, self).__init__()
self.sents_src = read_file(
"/content/drive/My Drive/ColabNotebooks/summary/extra_dict/train.src"
)
self.sents_tgt = read_file(
"/content/drive/My Drive/ColabNotebooks/summary/extra_dict/train.tgt"
)
self.sents_src = self.sents_src.split('\n')
self.sents_tgt = self.sents_tgt.split('\n')
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
class BertDataset(Dataset):
"""
针对特定数据集，定义一个相关的取数据的方式
"""
def __init__(self, data) :
## 一般init函数是加载所有数据
super(BertDataset, self).__init__()
self.data = data
print("data size is " + str(len(data)))
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
def __getitem__(self, i):
## 得到单个数据
# print(i)
single_data = self.data[i]
original_text = single_data[0]
ans_text = single_data[1]
token_ids, token_type_ids = self.tokenizer.encode(
original_text, ans_text, max_length=maxlen
)
output = {
"token_ids": token_ids,
"token_type_ids": token_type_ids,
}
return output
def __len__(self):
return len(self.data)
class BertDataset(Dataset):
def __init__(self):
super(BertDataset, self).__init__()
self.sents_src = read_file(
"/content/drive/My Drive/ColabNotebooks/summary/extra_dict/train.src"
)
self.sents_tgt = read_file(
"/content/drive/My Drive/ColabNotebooks/summary/extra_dict/train.tgt"
)
self.sents_src = self.sents_src.split('\n')
self.sents_tgt = self.sents_tgt.split('\n')
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
def __getitem__(self, i):
title = self.sents_tgt[i]
content = self.sents_src[i]
token_ids, token_type_ids = self.tokenizer.encode(content,
title,
max_length=maxlen)
output = {
"token_ids": token_ids,
"token_type_ids": token_type_ids,
}
return output
self.__getitem__(i + 1)
def __len__(self):
data_size = len(self.sents_src)
return data_size
class NERDataset(Dataset):
"""
针对特定数据集，定义一个相关的取数据的方式
"""
def __init__(self, sents_src, sents_tgt):
## 一般init函数是加载所有数据
super(NERDataset, self).__init__()
# 读原始数据
# self.sents_src, self.sents_tgt = read_corpus(poem_corpus_dir)
self.sents_src = sents_src
self.sents_tgt = sents_tgt
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
def __getitem__(self, i):
## 得到单个数据
# print(i)
src = self.sents_src[i]
tgt = self.sents_tgt[i]
token_ids, token_type_ids = self.tokenizer.encode(src)
output = {
"token_ids": token_ids,
"token_type_ids": token_type_ids,
"target_id": tgt
}
return output
def __len__(self):
return len(self.sents_src)
class BertDataset(Dataset):
"""
针对特定数据集，定义一个相关的取数据的方式
"""
def __init__(self, sents_src, sents_tgt, vocab_path):
## 一般init函数是加载所有数据
super(BertDataset, self).__init__()
# 读原始数据
# self.sents_src, self.sents_tgt = read_corpus(poem_corpus_dir)
self.sents_src = sents_src
self.sents_tgt = sents_tgt
self.word2idx = load_chinese_base_vocab(vocab_path, simplfied=True)
self.idx2word = {k: v for v, k in self.word2idx.items()}
self.tokenizer = Tokenizer(self.word2idx)
def __getitem__(self, i):
## 得到单个数据
src = self.sents_src[i]
tgt = self.sents_tgt[i]
token_ids, token_type_ids = self.tokenizer.encode(src, tgt)
output = {
"token_ids": token_ids,
"token_type_ids": token_type_ids,
}
return output
def __len__(self):
return len(self.sents_src)
def __init__(self):
# 加载数据
data_path = "./corpus/细粒度NER/train.json"
self.vocab_path = "./state_dict/roberta_wwm_vocab.txt" # roberta模型字典的位置
self.sents_src, self.sents_tgt = read_corpus(data_path)
self.model_name = "roberta" # 选择模型名字
self.model_path = "./state_dict/roberta_wwm_pytorch_model.bin" # roberta模型位置
self.recent_model_path = "" # 用于把已经训练好的模型继续训练
self.model_save_path = "./细粒度_bert_ner_model_crf.bin"
self.batch_size = 8
self.lr = 1e-5
self.crf_lr = 1e-2 ## crf层学习率为0.01
# 加载字典
self.word2idx = load_chinese_base_vocab(self.vocab_path)
self.tokenier = Tokenizer(self.word2idx)
# 判断是否有可用GPU
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device: " + str(self.device))
# 定义模型
self.bert_model = load_bert(self.vocab_path, model_name=self.model_name, model_class="sequence_labeling_crf", target_size=len(target))
## 加载预训练的模型参数～
load_model_params(self.bert_model, self.model_path)
# 将模型发送到计算设备(GPU或CPU)
self.bert_model.to(self.device)
# 声明需要优化的参数
crf_params = list(map(id, self.bert_model.crf_layer.parameters())) ## 单独把crf层参数拿出来
base_params = filter(lambda p: id(p) not in crf_params, self.bert_model.parameters())
self.optimizer = torch.optim.Adam([
{"params": base_params},
{"params": self.bert_model.crf_layer.parameters(), "lr": self.crf_lr}], lr=self.lr, weight_decay=1e-3)
# 声明自定义的数据加载器
dataset = NERDataset(self.sents_src, self.sents_tgt, self.vocab_path)
self.dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=True, collate_fn=collate_fn)
def __init__(self, vocab_path, model_name="roberta"):
super(Seq2SeqModel, self).__init__()
self.word2ix = load_chinese_base_vocab(vocab_path)
self.tokenizer = Tokenizer(self.word2ix)
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLMPredictionHead
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLMPredictionHead
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
else :
raise Exception("model_name_err")
self.hidden_dim = config.hidden_size
self.vocab_size = config.vocab_size
def ner_print(model, test_data):
model.eval()
idxtword = {v: k for k, v in word2idx.items()}
tokenier = Tokenizer(word2idx)
trans = model.state_dict()["crf_layer.trans"]
for text in test_data:
decode = []
text_encode, text_ids = tokenier.encode(text)
text_tensor = torch.tensor(text_encode, device=model.device).view(1, -1)
out = model(text_tensor).squeeze(0) # 其实是nodes
labels = viterbi_decode(out, trans)
starting = False
for l in labels:
if l > 0:
label = target[l.item()]
decode.append(label)
else :
decode.append("O")
flag = 0
res = {}
# print(decode)
# print(text)
decode_text = [idxtword[i] for i in text_encode]
for index, each_entity in enumerate(decode):
if each_entity != "O":
if flag != each_entity:
cur_text = decode_text[index]
if each_entity in res.keys():
res[each_entity].append(cur_text)
else :
res[each_entity] = [cur_text]
flag = each_entity
elif flag == each_entity:
res[each_entity][-1] += decode_text[index]
else :
flag = 0
print(res)
def __init__(self, vocab_path, target_size, model_name="roberta"):
super(BertClsClassifier, self).__init__()
self.word2ix = load_chinese_base_vocab(vocab_path)
self.tokenizer = Tokenizer(self.word2ix)
self.target_size = target_size
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
else:
raise Exception("model_name_err")
self.final_dense = nn.Linear(config.hidden_size, self.target_size)
def __init__(self, word2ix, target_size, model_name="roberta"):
super(BertSeqLabeling, self).__init__()
self.tokenizer = Tokenizer(word2ix)
self.target_size = target_size
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertPredictionHeadTransform
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.transform = BertPredictionHeadTransform(config)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertPredictionHeadTransform
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.transform = BertPredictionHeadTransform(config)
else:
raise Exception("model_name_err")
self.final_dense = nn.Linear(config.hidden_size, self.target_size)
def __init__(self):
# 加载数据
self.sents_src, self.sents_tgt = load_data("./res.txt")
self.tokenier = Tokenizer(word2idx)
# 判断是否有可用GPU
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
print("device: " + str(self.device))
# 定义模型
self.bert_model = load_bert(word2idx,
model_name=model_name,
model_class="sequence_labeling_crf",
target_size=len(target))
## 加载预训练的模型参数～
self.bert_model.load_pretrain_params(model_path,
keep_tokens=keep_tokens)
# 将模型发送到计算设备(GPU或CPU)
self.bert_model.to(self.device)
# 声明需要优化的参数
crf_params = list(map(
id, self.bert_model.crf_layer.parameters())) ## 单独把crf层参数拿出来
base_params = filter(lambda p: id(p) not in crf_params,
self.bert_model.parameters())
self.optimizer = torch.optim.Adam(
[{
"params": base_params
}, {
"params": self.bert_model.crf_layer.parameters(),
"lr": crf_lr
}],
lr=lr,
weight_decay=1e-5)
# 声明自定义的数据加载器
dataset = NERDataset(self.sents_src, self.sents_tgt)
self.dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=collate_fn)
class BertDataset(Dataset):
"""
针对特定数据集，定义一个相关的取数据的方式
"""
def __init__(self):
## 一般init函数是加载所有数据
super(BertDataset, self).__init__()
## 拿到所有文件名字
self.txts = glob.glob('./state_dict/THUCNews/*/*.txt')
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
def __getitem__(self, i):
## 得到单个数据
# print(i)
text_name = self.txts[i]
with open(text_name, "r", encoding="utf-8") as f:
text = f.read()
text = text.split('\n')
if len(text) > 1:
title = text[0]
content = '\n'.join(text[1:])
token_ids, token_type_ids = self.tokenizer.encode(
content, title, max_length=maxlen)
output = {
"token_ids": token_ids,
"token_type_ids": token_type_ids,
}
return output
self.__getitem__(i + 1)
def __len__(self):
return len(self.txts)
from bert_seq2seq.tokenizer import Tokenizer, load_chinese_base_vocab
from bert_seq2seq.utils import load_bert
import numpy as np
import time
vocab_path = "./state_dict/roberta_wwm_vocab.txt" # roberta模型字典的位置
model_name = "roberta" # 选择模型名字
model_path = "./state_dict/roberta_wwm_pytorch_model.bin" # roberta模型位置
recent_model_path = "" # 用于把已经训练好的模型继续训练
model_save_path = "./state_dict/bert_model_relation_extrac.bin"
batch_size = 16
lr = 1e-5
word2idx = load_chinese_base_vocab(vocab_path)
idx2word = {v: k for k, v in word2idx.items()}
tokenizer = Tokenizer(word2idx)
def load_data(filename):
D = []
with open(filename, encoding='utf-8') as f:
for l in f:
l = json.loads(l)
D.append({
'text':
l['text'],
'spo_list': [(spo['subject'], spo['predicate'], spo['object'])
for spo in l['spo_list']]
})
return D
class Seq2SeqModel(nn.Module):
"""
"""
def __init__(self, vocab_path, model_name="roberta"):
super(Seq2SeqModel, self).__init__()
self.word2ix = load_chinese_base_vocab(vocab_path)
self.tokenizer = Tokenizer(self.word2ix)
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLMPredictionHead
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLMPredictionHead
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
else :
raise Exception("model_name_err")
self.hidden_dim = config.hidden_size
self.vocab_size = config.vocab_size
def compute_loss(self, predictions, labels, target_mask):
"""
target_mask : 句子a部分和pad部分全为0， 而句子b部分为1
"""
predictions = predictions.view(-1, self.vocab_size)
labels = labels.view(-1)
target_mask = target_mask.view(-1).float()
loss = nn.CrossEntropyLoss(ignore_index=0, reduction="none")
return (loss(predictions, labels) * target_mask).sum() / target_mask.sum() ## 通过mask 取消 pad 和句子a部分预测的影响
def forward(self, input_tensor, token_type_id, position_enc=None, labels=None, device="cpu"):
## 传入输入，位置编码，token type id ，还有句子a 和句子b的长度，注意都是传入一个batch数据
## 传入的几个值，在seq2seq 的batch iter 函数里面都可以返回
input_shape = input_tensor.shape
batch_size = input_shape[0]
seq_len = input_shape[1]
## 构建特殊的mask
ones = torch.ones((1, 1, seq_len, seq_len), dtype=torch.float32, device=device)
a_mask = ones.tril() # 下三角矩阵
s_ex12 = token_type_id.unsqueeze(1).unsqueeze(2).float()
s_ex13 = token_type_id.unsqueeze(1).unsqueeze(3).float()
a_mask = (1.0 - s_ex12) * (1.0 - s_ex13) + s_ex13 * a_mask
enc_layers, _ = self.bert(input_tensor, position_ids=position_enc, token_type_ids=token_type_id, attention_mask=a_mask,
output_all_encoded_layers=True)
squence_out = enc_layers[-1] ## 取出来最后一层输出
predictions = self.decoder(squence_out)
if labels is not None:
## 计算loss
## 需要构建特殊的输出mask 才能计算正确的loss
# 预测的值不用取最后sep符号的结果 因此是到-1
predictions = predictions[:, :-1].contiguous()
target_mask = token_type_id[:, 1:].contiguous()
loss = self.compute_loss(predictions, labels, target_mask)
return predictions, loss
else :
return predictions
def generate(self, text, out_max_length=80, beam_size=1, device="cpu", is_poem=False):
# 对 一个 句子生成相应的结果
## 通过输出最大长度得到输入的最大长度，这里问题不大，如果超过最大长度会进行截断
self.out_max_length = out_max_length
input_max_length = max_length - out_max_length
# print(text)
token_ids, token_type_ids = self.tokenizer.encode(text, max_length=input_max_length)
token_ids = torch.tensor(token_ids, device=device).view(1, -1)
token_type_ids = torch.tensor(token_type_ids, device=device).view(1, -1)
if is_poem:## 古诗的beam-search稍有不同
out_puts_ids = self.poem_beam_search(token_ids, token_type_ids, self.word2ix, beam_size=beam_size, device=device)
else :
out_puts_ids = self.beam_search(token_ids, token_type_ids, self.word2ix, beam_size=beam_size, device=device)
# 解码 得到相应输出
return self.tokenizer.decode(out_puts_ids)
def poem_beam_search(self, token_ids, token_type_ids, word2ix, beam_size=1, device="cpu"):
"""
专门针对写诗的beam-search
"""
ix2word = {v: k for k, v in word2ix.items()}
sep_id = word2ix["[SEP]"]
douhao_id = word2ix["，"]# 逗号
juhao_id = word2ix["。"]# 句号
# 用来保存输出序列
output_ids = [[]]
word_list = {} # 保证不重复生成
last_chars = []
yayun_save = -1
# 用来保存累计得分
output_scores = torch.zeros(token_ids.shape[0], device=device)
flag = 0 # 判断第一次遇到逗号
for step in range(self.out_max_length):
scores = self.forward(token_ids, token_type_ids, device=device)
if step == 0:
# 重复beam-size次 输入ids
token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
token_type_ids = token_type_ids.view(1, -1).repeat(beam_size, 1)
## 计算log 分值 (beam_size, vocab_size)
logit_score = torch.log_softmax(scores, dim=-1)[:, -1]
logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
## 取topk的时候我们是展平了然后再去调用topk函数
# 展平
logit_score = logit_score.view(-1)
hype_score, hype_pos = torch.topk(logit_score, beam_size)
indice1 = hype_pos / scores.shape[-1] # 行索引
indice2 = hype_pos % scores.shape[-1] # 列索引
# 下面需要更新一下输出了
new_hype_scores = []
new_hype_ids = []
next_chars = [] # 用来保存新预测出来的一个字符，继续接到输入序列后面，再去预测新字符
index = 0
for i_1, i_2, score in zip(indice1, indice2, hype_score):
i_1 = i_1.item()
i_2 = i_2.item()
score = score.item()
if i_2 != douhao_id and i_2 != juhao_id:
if i_2 not in word_list.keys():
word_list[i_2] = 1
else :
# 加惩罚
word_list[i_2] += 1
score -= 1 * word_list[i_2]
hype_score[index] -= 1 * word_list[i_2]
if flag == 0 and i_2 == douhao_id:
if len(last_chars) - 1 < index:
# 说明刚开始预测便预测到逗号了，上一个字符还没有存储
break
flag += 1
word = ix2word[last_chars[index]]# 找到上一个字符 记住其押韵情况
for i, each_yayun in enumerate(yayun_list):
if word in each_yayun:
yayun_save = i
break
if i_2 == juhao_id:
word = ix2word[last_chars[index]]
# 找押韵 给奖励
if word in yayun_list[yayun_save]:
score += 5
hype_score[index] += 5
else:
score -= 2
hype_score[index] -= 2
hype_id = output_ids[i_1] + [i_2] # 保存所有输出的序列，而不仅仅是新预测的单个字符
if i_2 == sep_id:
# 说明解码到最后了
if score == torch.max(hype_score).item():
return hype_id[: -1]
else:
# 完成一个解码了，但这个解码得分并不是最高，因此的话需要跳过这个序列
beam_size -= 1
else :
new_hype_ids.append(hype_id)
new_hype_scores.append(score)
next_chars.append(i_2) # 收集一下，需要连接到当前的输入序列之后
index += 1
output_ids = new_hype_ids
last_chars = next_chars.copy() # 记录一下上一个字符
output_scores = torch.tensor(new_hype_scores, dtype=torch.float32, device=device)
# 现在需要重新构造输入数据了，用上一次输入连接上这次新输出的字符，再输入bert中预测新字符
token_ids = token_ids[:len(output_ids)].contiguous() # 截取，因为要过滤掉已经完成预测的序列
token_type_ids = token_type_ids[: len(output_ids)].contiguous()
next_chars = torch.tensor(next_chars, dtype=torch.long, device=device).view(-1, 1)
next_token_type_ids = torch.ones_like(next_chars, device=device)
# 连接
token_ids = torch.cat((token_ids, next_chars), dim=1)
token_type_ids = torch.cat((token_type_ids, next_token_type_ids), dim=1)
if beam_size < 1:
break
# 如果达到最大长度的话 直接把得分最高的输出序列返回把
return output_ids[output_scores.argmax().item()]
def beam_search(self, token_ids, token_type_ids, word2ix, beam_size=1, device="cpu"):
"""
beam-search操作
"""
sep_id = word2ix["[SEP]"]
# 用来保存输出序列
output_ids = [[]]
# 用来保存累计得分
output_scores = torch.zeros(token_ids.shape[0], device=device)
for step in range(self.out_max_length):
scores = self.forward(token_ids, token_type_ids, device=device)
if step == 0:
# 重复beam-size次 输入ids
token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
token_type_ids = token_type_ids.view(1, -1).repeat(beam_size, 1)
## 计算log 分值 (beam_size, vocab_size)
logit_score = torch.log_softmax(scores, dim=-1)[:, -1]
logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
## 取topk的时候我们是展平了然后再去调用topk函数
# 展平
logit_score = logit_score.view(-1)
hype_score, hype_pos = torch.topk(logit_score, beam_size)
indice1 = hype_pos / scores.shape[-1] # 行索引
indice2 = hype_pos % scores.shape[-1] # 列索引
# 下面需要更新一下输出了
new_hype_scores = []
new_hype_ids = []
# 为啥有这个[],就是因为要过滤掉结束的序列。
next_chars = [] # 用来保存新预测出来的一个字符，继续接到输入序列后面，再去预测新字符
for i_1, i_2, score in zip(indice1, indice2, hype_score):
i_1 = i_1.item()
i_2 = i_2.item()
score = score.item()
hype_id = output_ids[i_1] + [i_2] # 保存所有输出的序列，而不仅仅是新预测的单个字符
if i_2 == sep_id:
# 说明解码到最后了
if score == torch.max(hype_score).item():
# 说明找到得分最大的那个序列了 直接返回即可
return hype_id[: -1]
else:
# 完成一个解码了，但这个解码得分并不是最高，因此的话需要跳过这个序列
beam_size -= 1
else :
new_hype_ids.append(hype_id)
new_hype_scores.append(score)
next_chars.append(i_2) # 收集一下，需要连接到当前的输入序列之后
output_ids = new_hype_ids
output_scores = torch.tensor(new_hype_scores, dtype=torch.float32, device=device)
# 现在需要重新构造输入数据了，用上一次输入连接上这次新输出的字符，再输入bert中预测新字符
token_ids = token_ids[:len(output_ids)].contiguous() # 截取，因为要过滤掉已经完成预测的序列
token_type_ids = token_type_ids[: len(output_ids)].contiguous()
next_chars = torch.tensor(next_chars, dtype=torch.long, device=device).view(-1, 1)
next_token_type_ids = torch.ones_like(next_chars, device=device)
# 连接
token_ids = torch.cat((token_ids, next_chars), dim=1)
token_type_ids = torch.cat((token_type_ids, next_token_type_ids), dim=1)
if beam_size < 1:
break
# 如果达到最大长度的话 直接把得分最高的输出序列返回把
return output_ids[output_scores.argmax().item()]
class Trainer:
def __init__(self):
# 加载数据
data_path = "./corpus/新闻标题文本分类/Train.txt"
self.vocab_path = "./state_dict/roberta_wwm_vocab.txt" # roberta模型字典的位置
self.sents_src, self.sents_tgt = read_corpus(data_path)
self.model_name = "roberta" # 选择模型名字
self.model_path = "./state_dict/roberta_wwm_pytorch_model.bin" # roberta模型位置
self.recent_model_path = "" # 用于把已经训练好的模型继续训练
self.model_save_path = "./bert_multi_classify_model.bin"
self.batch_size = 16
self.lr = 1e-5
# 加载字典
self.word2idx = load_chinese_base_vocab(self.vocab_path)
self.tokenier = Tokenizer(self.word2idx)
# 判断是否有可用GPU
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
print("device: " + str(self.device))
# 定义模型
self.bert_model = load_bert(self.vocab_path,
model_name=self.model_name,
model_class="cls",
target_size=len(target))
## 加载预训练的模型参数～
load_model_params(self.bert_model, self.model_path)
# 将模型发送到计算设备(GPU或CPU)
self.bert_model.to(self.device)
# 声明需要优化的参数
self.optim_parameters = list(self.bert_model.parameters())
self.optimizer = torch.optim.Adam(self.optim_parameters,
lr=self.lr,
weight_decay=1e-3)
# 声明自定义的数据加载器
dataset = NLUDataset(self.sents_src, self.sents_tgt, self.vocab_path)
self.dataloader = DataLoader(dataset,
batch_size=self.batch_size,
shuffle=True,
collate_fn=collate_fn)
def train(self, epoch):
# 一个epoch的训练
self.bert_model.train()
self.iteration(epoch, dataloader=self.dataloader, train=True)
def save(self, save_path):
"""
保存模型
"""
torch.save(self.bert_model.state_dict(), save_path)
print("{} saved!".format(save_path))
def iteration(self, epoch, dataloader, train=True):
total_loss = 0
start_time = time.time() ## 得到当前时间
step = 0
for token_ids, token_type_ids, target_ids in tqdm(dataloader,
position=0,
leave=True):
step += 1
if step % 2000 == 0:
self.bert_model.eval()
test_data = [
"编剧梁馨月讨稿酬六六何念助阵 公司称协商解决", "西班牙BBVA第三季度净利降至15.7亿美元",
"基金巨亏30亿 欲打开云天系跌停自救"
]
for text in test_data:
text, text_ids = self.tokenier.encode(text)
text = torch.tensor(text, device=self.device).view(1, -1)
print(target[torch.argmax(self.bert_model(text)).item()])
self.bert_model.train()
token_ids = token_ids.to(self.device)
token_type_ids = token_type_ids.to(self.device)
target_ids = target_ids.to(self.device)
# 因为传入了target标签，因此会计算loss并且返回
predictions, loss = self.bert_model(
token_ids,
labels=target_ids,
)
# 反向传播
if train:
# 清空之前的梯度
self.optimizer.zero_grad()
# 反向传播, 获取新的梯度
loss.backward()
# 用获取的梯度更新模型参数
self.optimizer.step()
# 为计算当前epoch的平均loss
total_loss += loss.item()
end_time = time.time()
spend_time = end_time - start_time
# 打印训练信息
print("epoch is " + str(epoch) + ". loss is " + str(total_loss) +
". spend time is " + str(spend_time))
# 保存模型
self.save(self.model_save_path)
import numpy as np
import os
import json
import time
import bert_seq2seq
from bert_seq2seq.tokenizer import Tokenizer, load_chinese_base_vocab
from bert_seq2seq.utils import load_bert
relation_extrac_model = "./state_dict/bert_model_relation_extrac.bin"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
vocab_path = "./state_dict/roberta_wwm_vocab.txt" # roberta模型字典的位置
model_name = "roberta" # 选择模型名字
# model_path = "./state_dict/bert-base-chinese-pytorch_model.bin" # roberta模型位
# 加载字典
word2idx = load_chinese_base_vocab(vocab_path, simplfied=False)
tokenizer = Tokenizer(word2idx)
idx2word = {v: k for k, v in word2idx.items()}
predicate2id, id2predicate = {}, {}
with open('./corpus/三元组抽取/all_50_schemas') as f:
for l in f:
l = json.loads(l)
if l['predicate'] not in predicate2id:
id2predicate[len(predicate2id)] = l['predicate']
predicate2id[l['predicate']] = len(predicate2id)
def search(pattern, sequence):
"""从sequence中寻找子串pattern
如果找到，返回第一个下标；否则返回-1。
"""
