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NLP中基于Bert的数据预处理_bert数据预处理
作者：我家小花儿 | 2024-05-30 20:15:25
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bert数据预处理
1.中文命名实体识别
###1.原始数据格式
海 O 钓 O 比 O 赛 O 地 O 点 O 在 O 厦 B-LOC 门 I-LOC 与 O 金 B-LOC 门 I-LOC 之 O 间 O 的 O 海 O 域 O 。 O 日 B-LOC 俄 B-LOC 两 O 国 O 国 O 内 O 政 O 局 O 都 O 充 O 满 O 变 O 数 O ， O 尽 O 管 O 日 B-LOC 俄 B-LOC 关 O 系 O 目 O 前 O 是 O 历 O 史 O 最 O 佳 O 时 O 期 O ， O 但 O 其 O 脆 O 弱 O 性 O 不 O 言 O 自 O 明 O 。 O
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2.读取数据
将数据中的文本和对应的标签,读取成下面的one-hot格式
['海', '钓', '比', '赛', '地', '点', '在', '厦', '门', '与', '金', '门', '之', '间', '的', '海', '域', '。', ']
[ 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'O', 'B-LOC', 'I-LOC', 'O' , 'O', 'O', 'O', 'O', 'O']
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3.处理数据成为Bert的输入格式
首先创建一个标签的字典库
VOCAB=(“[PAD]”,“O”,“'B-LOC”,“I-LOC”,“[CLS]”,“[SEP]”)
其中[PAD]是一个补位符号,可以自己定义,也可以定义为x都可以,VOCAB对应的字典如下:
{0: ‘[PAD]’, 1: ‘O’, 2: ‘B-TIM’, 3: ‘I-TIM’, 4: ‘B-PER’, 5: ‘I-PER’, 6: ‘B-ORG’, 7: ‘I-ORG’, 8: ‘B-LOC’, 9: ‘I-LOC’, 10: ‘[CLS]’, 11: ‘[SEP]’}
然后根据这个字典处理成Bert需要的数据:
input_ids:将文本转换成bert字典中对应的编号
input_mask:用1和0代表两句话的位置,如果只有一句话,就用1表示(开始符,分割符也要占一个位置),0用来补位
segment_ids:表示句子的长度
label_ids:是实体标签对应字典中的位置
- 在输入的句子的前后需要加一个[CLS]和[SEP],将他们转换成id的格式,在bert的字典中[CLS]对应的是101,[SEP]对应的是102
- 在label也添加[CLS]和[SEP],标签库里[CLS]对应的是10,[SEP]对应的是11
转换成bert输入的格式如下:
'input_ids': [101, 3862, 7157, 3683, 6612, 1765, 4157, 1762, 1336, 7305, 680, 7032, 7305, 722, 7313, 4638, 3862, 1818, 511, 102]
'input_mask':[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
'segment_ids':[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
'label_ids':[10, 1, 1, 1, 1, 1, 1, 1, 8, 9, 1, 8, 9, 1, 1, 1, 1, 1, 1, 11]
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4.填补数据
因为输入到bert模型中的数据,每一个epoch的数据的长度要求一致,且长度不能超过512位,便于计算.
input_ids:在list后面添加0补位,因为在句子中是使用[PAD]进行补位,[PAD]在bert的字典库中对应的位置为0
input_mask:用0补位
segment_ids:用0补位
label_ids:用0补位,因为在我自己设置的tag字典库中补位是用[PAD],他对应的位置是0,这个可以自定义.
我设置的最大长度为100,转换成长度一致的结果如下:
input_ids=[101, 3862, 7157, 3683, 6612, 1765, 4157, 1762, 1336, 7305, 680, 7032, 7305, 722, 7313, 4638, 3862, 1818, 511, 102, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
input_mask=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
segment_ids=[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, 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]
label_ids=[10, 1, 1, 1, 1, 1, 1, 1, 8, 9, 1, 8, 9, 1, 1, 1, 1, 1, 1, 11, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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5.过程代码
为了便于理解,我将每一个过程都封装成了一个函数,代码如下
from transformers import BertTokenizer bert_model='./bert-base-chinese' tokenizer=BertTokenizer.from_pretrained(bert_model) # 自定义的label标签字典库 VOCAB = ( "[PAD]", "O", 'B-TIM', 'I-TIM', "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "[CLS]", "[SEP]" ) # 标签转数字,数字转标签 tag2idx = {tag: idx for idx, tag in enumerate(VOCAB)} idx2tag = {idx: tag for idx, tag in enumerate(VOCAB)} MAX_LEN=510 # 定义一个类分装好数据 class Input_futures(object): def __init__(self,input_ids,input_mask,segment_ids,label_ids): self.input_ids=input_ids self.input_mask=input_mask self.segment_ids=segment_ids self.label_ids=label_ids class Input_example(object): def __init__(self, input_ids, input_mask, segment_ids, label_ids): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_ids = label_ids def load_data(file_path): with open(file_path,'r',encoding='utf-8')as f: lines=f.read().strip().split('\n\n') sents, tags_li = [], [] # list of lists for i, entry in enumerate(lines): words = [line.split()[0] for line in entry.splitlines()] tags = ([line.split()[-1] for line in entry.splitlines()]) sents.append(words[:MAX_LEN]) tags_li.append(tags[:MAX_LEN]) return zip(sents,tags_li) def convert_to_feature(entitys): input_ids=[] input_mask=[] segment_ids=[] label_ids=[] for entity in entitys: input=tokenizer.encode_plus(entity[0]) #label的前面添加[CLS],尾部添加[SEP] label=list(entity[1]) label.insert(0,'[CLS]') label.append('[SEP]') label_id=[tag2idx[each] for each in label] input_ids.append(input['input_ids']) input_mask.append(input['attention_mask']) segment_ids.append(input['token_type_ids']) label_ids.append(label_id) feature = Input_futures( input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_ids=label_ids ) return feature def convrt_to_example(feature,max_len): f=lambda feature,max_len:[sample+[0]*(max_len-len(sample))for sample in feature] input_ids=f(feature.input_ids,max_len) input_mask=f(feature.input_mask,max_len) segment_ids=f(feature.segment_ids,max_len) label_ids=f(feature.label_ids,max_len) example=Input_example( input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_ids=label_ids ) return example if __name__=='__main__': print('程序开始'.center(50,'#')) # 1.读取数据 path='./data/train.txt' entitys=load_data(path) # 2.将数据处理成bert格式的类型 feature=convert_to_feature(entitys) # 3.填充数据 example=convrt_to_example(feature,100) print('程序结束'.center(50,'#'))
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6.分装成DataSet类
因为每次要去写代码比较麻烦,所以我将代码封装成了DataSet类,只要按照我们自己要求的格式进行输入就可以直接拿去用,省去了再写代码的过程.
- 前置工作
将数据封装成如下格式,并且保存成pkl格式
['海', '钓', '比', '赛', '地', '点', '在', '厦', '门', '与', '金', '门', '之', '间', '的', '海', '域', '。', ']
[ 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'O', 'B-LOC', 'I-LOC', 'O' , 'O', 'O', 'O', 'O', 'O']
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分装数据:
def load_data(file_path):
with open(file_path,'r',encoding='utf-8')as f:
lines=f.read().strip().split('\n\n')
sents, tags_li = [], [] # list of lists
for i, entry in enumerate(lines):
words = [line.split()[0] for line in entry.splitlines()]
tags = ([line.split()[-1] for line in entry.splitlines()])
sents.append(words[:MAX_LEN])
tags_li.append(tags[:MAX_LEN])
return zip(sents,tags_li)
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注意:我这里是将数据打包成了一个zip格式,到时候输出的时候,sent和tag会作为一个整体输出.
创建2个utils,保存成pkl格式,和读取pkl格式的数据
import pickle as pkl
import codecs
def save_pkl(path, obj):
print(f'save in {path}')
with codecs.open(path, 'wb') as f:
pkl.dump(obj, f)
def load_pkl(path):
print(f'load in {path}')
with codecs.open(path,'rb') as f:
data = pkl.load(f)
return data
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只要完成上面2步骤之后,数据保存成我指定的格式,然后把下面的封装好的代码放到项目中就可以直接使用,节省了很多步骤.
- 分装Dataset类
import torch from torch.utils.data import Dataset from transformers import BertTokenizer from utils.util import load_pkl bert_model='./bert-base-chinese' tokenizer=BertTokenizer.from_pretrained(bert_model) class NerDataset(Dataset): def __init__(self,file_path,tag2idx): self.tag2idx=tag2idx self.entities=list(load_pkl(file_path)) def __getitem__(self, item): entity=self.entities[item] feature = {} input = tokenizer.encode_plus(entity[0]) # label的前面添加[CLS],尾部添加[SEP] label = entity[1] label.insert(0, '[CLS]') label.append('[SEP]') label_id = [self.tag2idx[each] for each in label] feature['input_ids']=input['input_ids'] feature['attention_mask']=input['attention_mask'] feature['token_type_ids']=input['token_type_ids'] feature['label_ids']=label_id feature['len']=len(input['input_ids']) return feature def __len__(self): return len(self.entities) def collate_fn(batch): all_input_ids = [] all_attention_mask = [] all_token_type_ids = [] all_labels = [] # 计算这一个批次的最大长度 lens = [data['len'] for data in batch] max_len=max(lens) f = lambda feature, max_len: feature + [0] * (max_len - len(feature)) for feature in batch: input_ids = f(feature['input_ids'], max_len) attention_mask = f(feature['attention_mask'], max_len) token_type_ids = f(feature['token_type_ids'], max_len) label_ids = f(feature['label_ids'], max_len) all_input_ids.append(input_ids) all_attention_mask.append(attention_mask) all_token_type_ids.append(token_type_ids) all_labels.append(label_ids) # 最后将这些值转换为tensor格式进行输出 return torch.tensor(all_input_ids),torch.tensor(all_token_type_ids),torch.tensor(all_attention_mask),torch.tensor(all_labels)
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- 使用方法
from torch.utils.data import DataLoader from NER.NerDataSet import NerDataset, collate_fn VOCAB = ( "[PAD]", "O", 'B-TIM', 'I-TIM', "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "[CLS]", "[SEP]" ) # 标签转数字,数字转标签 tag2idx = {tag: idx for idx, tag in enumerate(VOCAB)} idx2tag = {idx: tag for idx, tag in enumerate(VOCAB)} MAX_LEN=510 if __name__=="__main__": print("程序开始".center(40,'#')) save_path = './data/train.pkl' train_data = NerDataset(file_path=save_path,tag2idx=tag2idx) train_iter=DataLoader(dataset=train_data, batch_size=4, shuffle=True, collate_fn=collate_fn) for i,batch in enumerate(train_iter): print(f'第{i}个batch') input_ids, token_type_ids, attention_mask, labels_ids = batch print(input_ids) print(token_type_ids) print(attention_mask) print(labels_ids)
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- 结果展示
因为我设置了batch_size是4,则一个batch中就只有四个数据,并且这些数据的顺序都是打乱的.
第0个batch tensor([[ 101, 1046, 7716, 2209, 4638, 1957, 1036, 6375, 2025, 791, 2399, 6438, 758, 2399, 5277, 8024, 1961, 2792, 1762, 4638, 4408, 677, 3300, 124, 121, 1914, 1399, 1398, 2110, 8024, 6421, 4408, 4638, 100, 2157, 1999, 833, 100, 4507, 122, 121, 1399, 2157, 7270, 5299, 2768, 511, 102, 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], [ 101, 3189, 915, 697, 1744, 1744, 1079, 3124, 2229, 6963, 1041, 4007, 1359, 3144, 8024, 2226, 5052, 3189, 915, 1068, 5143, 4680, 1184, 3221, 1325, 1380, 3297, 881, 3198, 3309, 8024, 852, 1071, 5546, 2483, 2595, 679, 6241, 5632, 3209, 511, 102, 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], [ 101, 852, 868, 711, 671, 702, 1066, 772, 1054, 1447, 510, 782, 3696, 1062, 789, 8024, 2418, 2496, 5541, 2577, 2160, 7333, 8024, 4696, 3633, 976, 1168, 100, 1044, 1921, 678, 722, 2569, 5445, 2569, 8024, 1400, 1921, 678, 722, 727, 5445, 727, 100, 8024, 3909, 1265, 702, 782, 4638, 1399, 1164, 2533, 1927, 1469, 2143, 6802, 2650, 1599, 8024, 2828, 3121, 7484, 1920, 689, 3030, 1762, 7674, 855, 8024, 6821, 3416, 2798, 5543, 6631, 6632, 5632, 2769, 8024, 3030, 5564, 686, 921, 8024, 3300, 2792, 868, 711, 511, 102], [ 101, 3763, 4294, 7339, 3136, 5298, 877, 7440, 2861, 8038, 697, 3118, 7339, 6963, 2682, 5526, 8024, 1728, 3634, 6963, 868, 1139, 749, 3297, 1920, 4638, 1222, 1213, 511, 102, 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, 0, 0]]) tensor([[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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0]]) tensor([[10, 4, 5, 5, 1, 1, 1, 4, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11, 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], [10, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11, 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], [10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11], [10, 6, 7, 7, 1, 1, 4, 5, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11, 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, 0, 0]]) 第1个batch tensor([[ 101, 6821, 2429, 898, 2255, 988, 3717, 4638, 1300, 4289, 7667, 4507, 1744, 1079, 671, 3837, 4638, 6392, 6369, 2360, 712, 2898, 6392, 6369, 8024, 3146, 702, 2456, 5029, 5408, 5125, 5401, 5445, 2612, 2131, 511, 102], [ 101, 3862, 7157, 3683, 6612, 1765, 4157, 1762, 1336, 7305, 680, 7032, 7305, 722, 7313, 4638, 3862, 1818, 511, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 101, 1762, 1355, 6809, 1744, 2157, 8024, 2593, 3131, 924, 7372, 1282, 1146, 3249, 1350, 8024, 2347, 2768, 711, 4852, 833, 924, 7397, 860, 5143, 4638, 7028, 6206, 5299, 2768, 6956, 1146, 511, 102, 0, 0, 0], [ 101, 1346, 1217, 3635, 6121, 4638, 3300, 4511, 3300, 1957, 8024, 3300, 2399, 6768, 782, 8024, 738, 3300, 704, 2399, 782, 511, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) tensor([[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, 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, 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]]) tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) tensor([[10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11], [10, 1, 1, 1, 1, 1, 1, 1, 8, 9, 1, 8, 9, 1, 1, 1, 1, 1, 1, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11, 0, 0, 0], [10, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
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5.总结
命名实体识别任务,是每个字对应一个标签,所以在填充数据的时候,需要给标签也填补位置.而bert输入格式是固定的,则在其他几个任务中,是一样的处理过程,唯一的区别就是在输出的真实值不一样.
2.文本分类
后续继续补充
3.多标签文本分类
后续继续补充
4.关系抽取
后续继续补充
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