TPLinker 联合抽取实体链接方式+源码分析

￡神魔★判官ぃ 2022-09-01 08:51 382阅读 0赞

关系抽取–TPLinker： [https://blog.csdn.net/weixin\_42223207/article/details/116425447][https_blog.csdn.net_weixin_42223207_article_details_116425447]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0YXlfZm9vbGlzaDEy_size_16_color_FFFFFF_t_70]  
Tagging  
TPLinker模型需要对关系三元组(subject, relation, object)进行手动Tagging，过程分为三部分：  
（1）entity head to entity tail (EH-TO-ET)  
（2）subject head to object head (SH-to-OH)  
（3）subject tail to object tail (ST-to-OT)  
标记示例见下图，EH-TO-ET用紫色表示，SH-to-OH用红色表示，ST-to-OT用蓝色表示。

论文笔记：路由器 TPLinker 也来做关系抽取：[https://zhuanlan.zhihu.com/p/304104571][https_zhuanlan.zhihu.com_p_304104571]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0YXlfZm9vbGlzaDEy_size_16_color_FFFFFF_t_70 1]  
关系抽取之TPLinker解读加源码分析：[https://zhuanlan.zhihu.com/p/342300800][https_zhuanlan.zhihu.com_p_342300800]

关于解码过程  
通过实体抽取得到字典D后，遍历关系，通过关系得到有关系的两个实体的尾部E， 再通过关系得到有关系的两个实体的头部，结合字典D得到这两个实体尾部set(s),set(o)(此为真实的标签），再通过set(s),set(o)在不在E里面，来判断是否成功抽取了一条三元组。  
其中相关的公式：  
文章的大概内容进行了解读，现在到了代码解读环节：  
输入数据部分：  
输入的seq的长度为seq\_len ，获取句子的最大长度

# train and valid max token num #

max_tok_num = 0
    all_data = train_data + valid_data 
        
    for sample in all_data:
        tokens = tokenize(sample["text"])
        max_tok_num = max(max_tok_num, len(tokens))
    max_tok_num # 获取句子的最大长度

接下来对文本超过长度的进行划窗处理：

if max_tok_num > hyper_parameters["max_seq_len"]: # 截断长度
        train_data = preprocessor.split_into_short_samples(train_data, 
                                                              hyper_parameters["max_seq_len"], 
                                                              sliding_len = hyper_parameters["sliding_len"], 
                                                              encoder = config["encoder"] #超过长度则滑动窗口得到新的样本
                                                             )                                           
        valid_data = preprocessor.split_into_short_samples(valid_data, 
                                                              hyper_parameters["max_seq_len"], 
                                                              sliding_len = hyper_parameters["sliding_len"], 
                                                              encoder = config["encoder"]
                                                             )

接下来看划窗的具体操作

def split_into_short_samples(self, sample_list, max_seq_len, sliding_len = 50, encoder = "BERT", data_type = "train"):
        new_sample_list = []
        for sample in tqdm(sample_list, desc = "Splitting into subtexts"):
            text_id = sample["id"]
            text = sample["text"]
            tokens = self._tokenize(text)
            tok2char_span = self._get_tok2char_span_map(text) #返回句子中单词的偏移量
    
            # sliding at token level
            split_sample_list = []
            for start_ind in range(0, len(tokens), sliding_len): #sliding_len 滑动窗口的大小，
                if encoder == "BERT": # if use bert, do not split a word into two samples
                    while "##" in tokens[start_ind]:
                        start_ind -= 1
                end_ind = start_ind + max_seq_len # 结束的长度
    
                char_span_list = tok2char_span[start_ind:end_ind] #截断
                char_level_span = [char_span_list[0][0], char_span_list[-1][1]] #第一个词到最后一个词的长度
                sub_text = text[char_level_span[0]:char_level_span[1]]#原始文本截断
    
                new_sample = {
        
                    "id": text_id,
                    "text": sub_text,
                    "tok_offset": start_ind, #token的偏移量
                    "char_offset": char_level_span[0], #每个字符的偏移量
                    }
                if data_type == "test": # test set
                    if len(sub_text) > 0:
                        split_sample_list.append(new_sample)
                else: # train or valid dataset, only save spo and entities in the subtext
                    # spo
                    sub_rel_list = []
                    for rel in sample["relation_list"]:
                        subj_tok_span = rel["subj_tok_span"]
                        obj_tok_span = rel["obj_tok_span"]
                        # if subject and object are both in this subtext, add this spo to new sample
                        if subj_tok_span[0] >= start_ind and subj_tok_span[1] <= end_ind \
                            and obj_tok_span[0] >= start_ind and obj_tok_span[1] <= end_ind: 
                            new_rel = copy.deepcopy(rel)
                            new_rel["subj_tok_span"] = [subj_tok_span[0] - start_ind, subj_tok_span[1] - start_ind] # start_ind: 单词级别的偏移量
                            new_rel["obj_tok_span"] = [obj_tok_span[0] - start_ind, obj_tok_span[1] - start_ind]
                            new_rel["subj_char_span"][0] -= char_level_span[0] # 字符级别的偏移量
                            new_rel["subj_char_span"][1] -= char_level_span[0]
                            new_rel["obj_char_span"][0] -= char_level_span[0]
                            new_rel["obj_char_span"][1] -= char_level_span[0]
                            sub_rel_list.append(new_rel)
                    
                    # entity
                    sub_ent_list = []
                    for ent in sample["entity_list"]:
                        tok_span = ent["tok_span"]
                        # if entity in this subtext, add the entity to new sample
                        if tok_span[0] >= start_ind and tok_span[1] <= end_ind: 
                            new_ent = copy.deepcopy(ent)
                            new_ent["tok_span"] = [tok_span[0] - start_ind, tok_span[1] - start_ind]
                            
                            new_ent["char_span"][0] -= char_level_span[0]
                            new_ent["char_span"][1] -= char_level_span[0]
    
                            sub_ent_list.append(new_ent)
                    
                    # event
                    if "event_list" in sample:
                        sub_event_list = []
                        for event in sample["event_list"]:
                            trigger_tok_span = event["trigger_tok_span"]
                            if trigger_tok_span[1] > end_ind or trigger_tok_span[0] < start_ind:
                                continue
                            new_event = copy.deepcopy(event)
                            new_arg_list = []
                            for arg in new_event["argument_list"]:
                                if arg["tok_span"][0] >= start_ind and arg["tok_span"][1] <= end_ind:
                                    new_arg_list.append(arg)
                            new_event["argument_list"] = new_arg_list
                            sub_event_list.append(new_event)
                        new_sample["event_list"] = sub_event_list # maybe empty
                        
                    new_sample["entity_list"] = sub_ent_list # maybe empty
                    new_sample["relation_list"] = sub_rel_list # maybe empty
                    split_sample_list.append(new_sample)
                
                # all segments covered, no need to continue
                if end_ind > len(tokens):
                    break
                    
            new_sample_list.extend(split_sample_list)
        return new_sample_list

输入数据，DataMaker4Bert中定义：

class DataMaker4Bert():
        def __init__(self, tokenizer, handshaking_tagger):
            self.tokenizer = tokenizer
            self.handshaking_tagger = handshaking_tagger
        
        def get_indexed_data(self, data, max_seq_len, data_type = "train"): #index转换为data
            indexed_samples = []
            for ind, sample in tqdm(enumerate(data), desc = "Generate indexed train or valid data"):
                text = sample["text"]
                # codes for bert input
                codes = self.tokenizer.encode_plus(text, 
                                        return_offsets_mapping = True, 
                                        add_special_tokens = False,
                                        max_length = max_seq_len, 
                                        truncation = True,
                                        pad_to_max_length = True)
    
    
                # tagging
                spots_tuple = None
                if data_type != "test":
                    spots_tuple = self.handshaking_tagger.get_spots(sample) #获取实体,头，尾标签
    
                # get codes
                input_ids = torch.tensor(codes["input_ids"]).long()
                attention_mask = torch.tensor(codes["attention_mask"]).long()
                token_type_ids = torch.tensor(codes["token_type_ids"]).long()
                tok2char_span = codes["offset_mapping"]
    
                sample_tp = (sample,
                         input_ids,
                         attention_mask,
                         token_type_ids,
                         tok2char_span,
                         spots_tuple,
                        )
                indexed_samples.append(sample_tp)       
            return indexed_samples

输入的是tokenizer和handshakingtagger，tokenizer为bert等一系列模型的标准输入，而get\_spots函数获取了实体，头，尾的标签，具体看下代码

def get_spots(self, sample):
            '''
            entity spot and tail_rel spot: (span_pos1, span_pos2, tag_id)
            head_rel spot: (rel_id, span_pos1, span_pos2, tag_id)
            '''
            ent_matrix_spots, head_rel_matrix_spots, tail_rel_matrix_spots = [], [], [] 
    
            for rel in sample["relation_list"]:
                subj_tok_span = rel["subj_tok_span"]
                obj_tok_span = rel["obj_tok_span"]
                ent_matrix_spots.append((subj_tok_span[0], subj_tok_span[1] - 1, self.tag2id_ent["ENT-H2T"])) #sub token的[起始位置，尾部位置，实体标签（1)]
                ent_matrix_spots.append((obj_tok_span[0], obj_tok_span[1] - 1, self.tag2id_ent["ENT-H2T"]))# obj token的[起始位置，尾部位置，实体标签（1)]
    
                if  subj_tok_span[0] <= obj_tok_span[0]:
                    head_rel_matrix_spots.append((self.rel2id[rel["predicate"]], subj_tok_span[0], obj_tok_span[0], self.tag2id_head_rel["REL-SH2OH"]))#【关系类别，实体_1 头部，实体_2头部，关系标签（1）】
                else:
                    head_rel_matrix_spots.append((self.rel2id[rel["predicate"]], obj_tok_span[0], subj_tok_span[0], self.tag2id_head_rel["REL-OH2SH"]))#【关系类别，实体_1 头部，实体_2头部，关系标签（2）】
                    
                if subj_tok_span[1] <= obj_tok_span[1]:
                    tail_rel_matrix_spots.append((self.rel2id[rel["predicate"]], subj_tok_span[1] - 1, obj_tok_span[1] - 1, self.tag2id_tail_rel["REL-ST2OT"]))#【关系类别，实体_1 尾部，实体_2尾部，关系标签（1）】
                else:
                    tail_rel_matrix_spots.append((self.rel2id[rel["predicate"]], obj_tok_span[1] - 1, subj_tok_span[1] - 1, self.tag2id_tail_rel["REL-OT2ST"]))#【关系类别，实体_1 尾部，实体_2尾部，关系标签（2）】
                    
            return ent_matrix_spots, head_rel_matrix_spots, tail_rel_matrix_spots
    获取输入的数据
    
    indexed_train_data = data_maker.get_indexed_data(train_data, max_seq_len) #获取输入
    # index_train_data = data_maker.get_indexed_data(train_test_data,max_seq_len)
    indexed_valid_data = data_maker.get_indexed_data(valid_data, max_seq_len)
    tokenizer = BertTokenizerFast.from_pretrained(config["bert_path"], add_special_tokens = False, do_lower_case = False)
    data_maker = DataMaker4Bert(tokenizer, handshaking_tagger) #(sample,input_ids，attention_mask,token_type_ids,tok2char_span,spots_tuple,)

接下来则是定义HandshakingTaggingScheme

max_seq_len = min(max_tok_num, hyper_parameters["max_seq_len"]) #max_len 长度
    rel2id = json.load(open(rel2id_path, "r", encoding = "utf-8")) 
    handshaking_tagger = HandshakingTaggingScheme(rel2id = rel2id, max_seq_len = max_seq_len) #初始化

查看具体的定义

class HandshakingTaggingScheme(object):
        """docstring for HandshakingTaggingScheme"""
        def __init__(self, rel2id, max_seq_len):
            super(HandshakingTaggingScheme, self).__init__()
            self.rel2id = rel2id
            self.id2rel = {
        ind:rel for rel, ind in rel2id.items()}
    
            self.tag2id_ent = {
         #实体头尾
                "O": 0,
                "ENT-H2T": 1, # entity head to entity tail
            }
            self.id2tag_ent = {
        id_:tag for tag, id_ in self.tag2id_ent.items()}
    
            self.tag2id_head_rel = {
         #sub,obj头对头标识1，obj头对sub头标识2
                "O": 0,
                "REL-SH2OH": 1, # subject head to object head
                "REL-OH2SH": 2, # object head to subject head
            }
            self.id2tag_head_rel = {
        id_:tag for tag, id_ in self.tag2id_head_rel.items()}
    
            self.tag2id_tail_rel = {
        
                "O": 0,    
                "REL-ST2OT": 1, # subject tail to object tail
                "REL-OT2ST": 2, # object tail to subject tail
            }
            self.id2tag_tail_rel = {
        id_:tag for tag, id_ in self.tag2id_tail_rel.items()}
    
            # mapping shaking sequence and matrix
            self.matrix_size = max_seq_len
            # e.g. [(0, 0), (0, 1), (0, 2), (1, 1), (1, 2), (2, 2)] #转换成矩阵上三角矩阵平铺
            self.shaking_ind2matrix_ind = [(ind, end_ind) for ind in range(self.matrix_size) for end_ind in list(range(self.matrix_size))[ind:]]
    
            self.matrix_ind2shaking_ind = [[0 for i in range(self.matrix_size)] for j in range(self.matrix_size)]
            for shaking_ind, matrix_ind in enumerate(self.shaking_ind2matrix_ind): #上三角矩阵，上三角每个元素储存着上三角铺平序列的相对应的位置序号
                self.matrix_ind2shaking_ind[matrix_ind[0]][matrix_ind[1]] = shaking_ind

这里比较关键的是shaking\_ind2matrix\_ind，与matrix\_ind2shaking\_ind，其中shaking\_ind2matrix\_ind如下所示，是一个上三角铺平序列

\[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (0, 13), …\]  
而matrix\_ind2shaking\_ind为优化前的二维矩阵，其中上三角每个元素储存着上三角铺平序列的相对应的位置序号

\[\[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], …\]  
最后组成上三角矩阵（打印的结果没有填满）

\[\[0, 1, 2, 3, 4, 5, 6, 7, 8, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], \[0, 0, 0, 0, 0, 0, 0, 0, 0, …\], …\]  
载入数据

train_dataloader = DataLoader(MyDataset(indexed_train_data), 
                                      batch_size = hyper_parameters["batch_size"], 
                                      shuffle = False, 
                                      num_workers = 5,
                                      drop_last = False,
                                      collate_fn = data_maker.generate_batch,

)

查看DataLoader的返回值：

def generate_batch(self, batch_data, data_type = "train"):
            sample_list = []
            input_ids_list = []
            attention_mask_list = []
            token_type_ids_list = [] 
            tok2char_span_list = []
            
            ent_spots_list = []
            head_rel_spots_list = []
            tail_rel_spots_list = []
    
            for tp in batch_data:
                sample_list.append(tp[0])
                input_ids_list.append(tp[1])
                attention_mask_list.append(tp[2])        
                token_type_ids_list.append(tp[3])        
                tok2char_span_list.append(tp[4])
                
                if data_type != "test":
                    ent_matrix_spots, head_rel_matrix_spots, tail_rel_matrix_spots = tp[5]
                    ent_spots_list.append(ent_matrix_spots)
                    head_rel_spots_list.append(head_rel_matrix_spots)
                    tail_rel_spots_list.append(tail_rel_matrix_spots)
    
            # @specific: indexed by bert tokenizer
            batch_input_ids = torch.stack(input_ids_list, dim = 0)
            batch_attention_mask = torch.stack(attention_mask_list, dim = 0)
            batch_token_type_ids = torch.stack(token_type_ids_list, dim = 0)
            
            batch_ent_shaking_tag, batch_head_rel_shaking_tag, batch_tail_rel_shaking_tag = None, None, None
            if data_type != "test":
                batch_ent_shaking_tag = self.handshaking_tagger.sharing_spots2shaking_tag4batch(ent_spots_list)
                batch_head_rel_shaking_tag = self.handshaking_tagger.spots2shaking_tag4batch(head_rel_spots_list)
                batch_tail_rel_shaking_tag = self.handshaking_tagger.spots2shaking_tag4batch(tail_rel_spots_list)
    
            return sample_list, \
                  batch_input_ids, batch_attention_mask, batch_token_type_ids, tok2char_span_list, \
                    batch_ent_shaking_tag, batch_head_rel_shaking_tag, batch_tail_rel_shaking_tag

其中比较重要的是batch\_ent\_shaking\_tag, batch\_head\_rel\_shaking\_tag, batch\_tail\_rel\_shaking\_tag这三个函数,在实体batch\_ent\_shaking\_tag中用到了handshaking\_tagger的sharing\_spots2shaking\_tag4batch的函数

def sharing_spots2shaking_tag4batch(self, batch_spots):
            '''
            convert spots to batch shaking seq tag
            因长序列的stack是费时操作，所以写这个函数用作生成批量shaking tag
            如果每个样本生成一条shaking tag再stack，一个32的batch耗时1s，太昂贵
            spots: [(start_ind, end_ind, tag_id), ], for entiy
            return: 
                batch_shake_seq_tag: (batch_size, shaking_seq_len)
            '''
            shaking_seq_len = self.matrix_size * (self.matrix_size + 1) // 2
            batch_shaking_seq_tag = torch.zeros(len(batch_spots), shaking_seq_len).long()
            for batch_id, spots in enumerate(batch_spots):
                for sp in spots:
                    shaking_ind = self.matrix_ind2shaking_ind[sp[0]][sp[1]] #在矩阵中找到实体的的start_int，跟end_int的位置值
                    tag_id = sp[2]
                    batch_shaking_seq_tag[batch_id][shaking_ind] = tag_id #铺平上三角矩阵，标注序列中的实体标识1的位置
            return batch_shaking_seq_tag

在关系batch\_head\_rel\_shaking\_tag与batch\_tail\_rel\_shaking\_tag中用到了spots2shaking\_tag4batch

def spots2shaking\_tag4batch(self, batch\_spots):  
‘’’  
convert spots to batch shaking seq tag  
spots: \[(rel\_id, start\_ind, end\_ind, tag\_id), \], for head relation and tail\_relation  
return:  
batch\_shake\_seq\_tag: (batch\_size, rel\_size, shaking\_seq\_len)  
‘’’  
shaking\_seq\_len = self.matrix\_size \* (self.matrix\_size + 1) // 2  
batch\_shaking\_seq\_tag = torch.zeros(len(batch\_spots), len(self.rel2id), shaking\_seq\_len).long()  
for batch\_id, spots in enumerate(batch\_spots):  
for sp in spots:  
shaking\_ind = self.matrix\_ind2shaking\_ind\[sp\[1\]\]\[sp\[2\]\]  
tag\_id = sp\[3\]  
rel\_id = sp\[0\]  
batch\_shaking\_seq\_tag\[batch\_id\]\[rel\_id\]\[shaking\_ind\] = tag\_id  
return batch\_shaking\_seq\_tag

跟实体类似，只是多了关系总数，然后整个函数的返回为

return sample\_list,  
batch\_input\_ids, tok2char\_span\_list,  
batch\_ent\_shaking\_tag, batch\_head\_rel\_shaking\_tag, batch\_tail\_rel\_shaking\_tag

初始化模型

rel\_extractor = TPLinkerBert(encoder,  
len(rel2id),  
hyper\_parameters\[“shaking\_type”\],  
hyper\_parameters\[“inner\_enc\_type”\],  
hyper\_parameters\[“dist\_emb\_size”\],  
hyper\_parameters\[“ent\_add\_dist”\],  
hyper\_parameters\[“rel\_add\_dist”\],  
)  
模型的具体定义

class TPLinkerBert(nn.Module):
        def __init__(self, encoder, 
                     rel_size, 
                     shaking_type,
                     inner_enc_type,
                     dist_emb_size,
                     ent_add_dist,
                     rel_add_dist
                    ):
            super().__init__()
            self.encoder = encoder
            hidden_size = encoder.config.hidden_size
            
            self.ent_fc = nn.Linear(hidden_size, 2) #实体预测，0，1
            self.head_rel_fc_list = [nn.Linear(hidden_size, 3) for _ in range(rel_size)] #rel_size多少种关系
            self.tail_rel_fc_list = [nn.Linear(hidden_size, 3) for _ in range(rel_size)]# 对每个关系进行个linear层的3分类【0，1，2】
            
            for ind, fc in enumerate(self.head_rel_fc_list):
                self.register_parameter("weight_4_head_rel{}".format(ind), fc.weight) #过3层全连接层
                self.register_parameter("bias_4_head_rel{}".format(ind), fc.bias) #偏差
            for ind, fc in enumerate(self.tail_rel_fc_list):
                self.register_parameter("weight_4_tail_rel{}".format(ind), fc.weight)
                self.register_parameter("bias_4_tail_rel{}".format(ind), fc.bias)
                
            # handshaking kernel
            self.handshaking_kernel = HandshakingKernel(hidden_size, shaking_type, inner_enc_type)
            
            # distance embedding
            self.dist_emb_size = dist_emb_size
            self.dist_embbedings = None # it will be set in the first forwarding
            
            self.ent_add_dist = ent_add_dist
            self.rel_add_dist = rel_add_dist

self.head\_rel\_fc\_list与self.tail\_rel\_fc\_list相同，是关系的一个全连接（标签有三个0,1,2），每一种关系有一个独立的MLP层，self.head\_rel\_fc\_list是列表的形式。

上面为关系和实体关系预测，实体和各个关系都经过了mlp层，我们假设有5种关系，则会有11层MLP即为：1个实体预测层+（1个头部层+1个尾部层）\*5

def forward(self, input_ids, attention_mask, token_type_ids):
            # input_ids, attention_mask, token_type_ids: (batch_size, seq_len)
            context_outputs = self.encoder(input_ids, attention_mask, token_type_ids) # 0 last_hidden 1 pooled
            # last_hidden_state: (batch_size, seq_len, hidden_size)
            last_hidden_state = context_outputs[0]
            
            # shaking_hiddens: (batch_size, 1 + ... + seq_len, hidden_size)
            shaking_hiddens = self.handshaking_kernel(last_hidden_state) #铺平上三角矩阵
            shaking_hiddens4ent = shaking_hiddens
            shaking_hiddens4rel = shaking_hiddens
            
            # add distance embeddings if it is set
            if self.dist_emb_size != -1:
                # set self.dist_embbedings
                hidden_size = shaking_hiddens.size()[-1]
                if self.dist_embbedings is None:
                    dist_emb = torch.zeros([self.dist_emb_size, hidden_size]).to(shaking_hiddens.device)
                    for d in range(self.dist_emb_size):
                        for i in range(hidden_size):
                            if i % 2 == 0:
                                dist_emb[d][i] = math.sin(d / 10000**(i / hidden_size))
                            else:
                                dist_emb[d][i] = math.cos(d / 10000**((i - 1) / hidden_size))
                    seq_len = input_ids.size()[1]
                    dist_embbeding_segs = []
                    for after_num in range(seq_len, 0, -1): #铺平
                        dist_embbeding_segs.append(dist_emb[:after_num, :])
                    self.dist_embbedings = torch.cat(dist_embbeding_segs, dim = 0)
                
                if self.ent_add_dist:
                    shaking_hiddens4ent = shaking_hiddens + self.dist_embbedings[None,:,:].repeat(shaking_hiddens.size()[0], 1, 1)
                if self.rel_add_dist:
                    shaking_hiddens4rel = shaking_hiddens + self.dist_embbedings[None,:,:].repeat(shaking_hiddens.size()[0], 1, 1)
                    
    #         if self.dist_emb_size != -1 and self.ent_add_dist:
    #             shaking_hiddens4ent = shaking_hiddens + self.dist_embbedings[None,:,:].repeat(shaking_hiddens.size()[0], 1, 1)
    #         else:
    #             shaking_hiddens4ent = shaking_hiddens
    #         if self.dist_emb_size != -1 and self.rel_add_dist:
    #             shaking_hiddens4rel = shaking_hiddens + self.dist_embbedings[None,:,:].repeat(shaking_hiddens.size()[0], 1, 1)
    #         else:
    #             shaking_hiddens4rel = shaking_hiddens
                
            ent_shaking_outputs = self.ent_fc(shaking_hiddens4ent) #实体预测，（0，1）
                
            head_rel_shaking_outputs_list = []
            nn.ModuleList()
            for fc in self.head_rel_fc_list:
                head_rel_shaking_outputs_list.append(fc(shaking_hiddens4rel)) #对每一种关系头进行分类
                
            tail_rel_shaking_outputs_list = []
            for fc in self.tail_rel_fc_list: #对每一种关系尾进行分类
                tail_rel_shaking_outputs_list.append(fc(shaking_hiddens4rel))
            
            head_rel_shaking_outputs = torch.stack(head_rel_shaking_outputs_list, dim = 1) #n种关系拼接在一起
            tail_rel_shaking_outputs = torch.stack(tail_rel_shaking_outputs_list, dim = 1) #n种关系拼接在一起
            
            return ent_shaking_outputs, head_rel_shaking_outputs, tail_rel_shaking_outputs

ent\_shaking\_outputs为实体预测，head\_rel\_shaking\_outputs\_list对关系头进行分类，tail\_rel\_shaking\_outputs\_list对关系尾进行分类，其中关键函数shaking\_hiddens4ent中的HandshakingKernel函数定义如下

class HandshakingKernel(nn.Module):
        def __init__(self, hidden_size, shaking_type, inner_enc_type):
            super().__init__()
            self.shaking_type = shaking_type
            if shaking_type == "cat":
                self.combine_fc = nn.Linear(hidden_size * 2, hidden_size) #fc层
            elif shaking_type == "cat_plus":
                self.combine_fc = nn.Linear(hidden_size * 3, hidden_size)
            elif shaking_type == "cln":
                self.tp_cln = LayerNorm(hidden_size, hidden_size, conditional = True)
            elif shaking_type == "cln_plus":
                self.tp_cln = LayerNorm(hidden_size, hidden_size, conditional = True)
                self.inner_context_cln = LayerNorm(hidden_size, hidden_size, conditional = True)
                
            self.inner_enc_type = inner_enc_type #一层单向lstm
            if inner_enc_type == "mix_pooling":
                self.lamtha = Parameter(torch.rand(hidden_size))
            elif inner_enc_type == "lstm":
                self.inner_context_lstm = nn.LSTM(hidden_size, 
                               hidden_size, 
                               num_layers = 1, 
                               bidirectional = False, 
                               batch_first = True)
         
        def enc_inner_hiddens(self, seq_hiddens, inner_enc_type = "lstm"):
            # seq_hiddens: (batch_size, seq_len, hidden_size)
            def pool(seqence, pooling_type):
                if pooling_type == "mean_pooling":
                    pooling = torch.mean(seqence, dim = -2)
                elif pooling_type == "max_pooling":
                    pooling, _ = torch.max(seqence, dim = -2)
                elif pooling_type == "mix_pooling":
                    pooling = self.lamtha * torch.mean(seqence, dim = -2) + (1 - self.lamtha) * torch.max(seqence, dim = -2)[0]
                return pooling
            if "pooling" in inner_enc_type:
                inner_context = torch.stack([pool(seq_hiddens[:, :i+1, :], inner_enc_type) for i in range(seq_hiddens.size()[1])], dim = 1)
            elif inner_enc_type == "lstm":
                inner_context, _ = self.inner_context_lstm(seq_hiddens)
                
            return inner_context
        def forward(self, seq_hiddens):
            '''
            seq_hiddens: (batch_size, seq_len, hidden_size)
            return:
            shaking_hiddenss: (batch_size, (1 + seq_len) * seq_len / 2, hidden_size) (32, 5+4+3+2+1, 5)
            '''#一句话中每个字与剩下的字构成上三角矩阵如：长度为5则的到的为[[batch,5,hidden_size],[batch,4,hidden_size]...]
            seq_len = seq_hiddens.size()[-2] #句子的长度
            shaking_hiddens_list = []
            for ind in range(seq_len):
                hidden_each_step = seq_hiddens[:, ind, :] #取每个batch的每个字的维度
                visible_hiddens = seq_hiddens[:, ind:, :] # 从当前取到最后
                repeat_hiddens = hidden_each_step[:, None, :].repeat(1, seq_len - ind, 1) #复制dim=1的维度跟visible维度保持一致
                if self.shaking_type == "cat":#选择的是cat模式，可以在配置文件中设置
                    shaking_hiddens = torch.cat([repeat_hiddens, visible_hiddens], dim = -1) #将当前每个字的维度与其后的每个字的维度拼接在一起
                    shaking_hiddens = torch.tanh(self.combine_fc(shaking_hiddens))#过一个线性层
                elif self.shaking_type == "cat_plus":
                    inner_context = self.enc_inner_hiddens(visible_hiddens, self.inner_enc_type)
                    shaking_hiddens = torch.cat([repeat_hiddens, visible_hiddens, inner_context], dim = -1)
                    shaking_hiddens = torch.tanh(self.combine_fc(shaking_hiddens))
                elif self.shaking_type == "cln":
                    shaking_hiddens = self.tp_cln(visible_hiddens, repeat_hiddens)
                elif self.shaking_type == "cln_plus":
                    inner_context = self.enc_inner_hiddens(visible_hiddens, self.inner_enc_type)
                    shaking_hiddens = self.tp_cln(visible_hiddens, repeat_hiddens)
                    shaking_hiddens = self.inner_context_cln(shaking_hiddens, inner_context)
                shaking_hiddens_list.append(shaking_hiddens) #添加到列表中
            long_shaking_hiddens = torch.cat(shaking_hiddens_list, dim = 1)#铺平上三角矩阵
            return long_shaking_hiddens

输入的seq\_hiddens维度是\[batch,seq\_len, hiddensize\]，是一句话经过bert编码过后的值，而HandshakingKernel函数的作用是将矩阵变为上三角矩阵，即本身矩阵为\[seq\_len \* seq\_len\]，在经过函数过后为每一行都减1，最后通过long\_shakinghiddens把函数把结果铺平，得到\[seq\_len+(seq\_len -1) + (seq\_len -2)…+1\],对应了图片部分。

整个函数先是循环每句话中的词，当ind是0时，hidden\_each\_step代表了循环的每个词的编码\[batch,1,hidden\_size\]，visiblehiddens是循环到的这个单词以及之后的单词的编码，维度就是\[batch,seq\_len,hidden\_size\],repeat\_hiddens对hidden\_each\_step的第二个维度进行了复制，维度为\[batch,seq\_len,hidden\_size\]，将当前单词和其后的各个单词的编码进行拼接维度是\[batch,seq\_len,hidden\_size\*2\]组成上三角矩阵的一行,在经过MLP层后shakinghiddens的维度是\[batch,seq\_len,hidden\_size\]，之后每一行依次类推。

关于loss部分

total_loss, total_ent_sample_acc, total_head_rel_sample_acc, total_tail_rel_sample_acc = 0., 0., 0., 0.
            for batch_ind, batch_train_data in enumerate(dataloader):
                t_batch = time.time()
                z = (2 * len(rel2id) + 1) # 2倍的关系
                steps_per_ep = len(dataloader) #有多少数据
                total_steps = hyper_parameters["loss_weight_recover_steps"] + 1 # + 1 avoid division by zero error #加速loss在一定的步数回归
                current_step = steps_per_ep * ep + batch_ind # ？
                w_ent = max(1 / z + 1 - current_step / total_steps, 1 / z)
                w_rel = min((len(rel2id) / z) * current_step / total_steps, (len(rel2id) / z))
                loss_weights = {
        "ent": w_ent, "rel": w_rel} #给予不同任务的权重
                
                loss, ent_sample_acc, head_rel_sample_acc, tail_rel_sample_acc = train_step(batch_train_data, optimizer, loss_weights)
                scheduler.step()
                
                total_loss += loss
                total_ent_sample_acc += ent_sample_acc
                total_head_rel_sample_acc += head_rel_sample_acc
                total_tail_rel_sample_acc += tail_rel_sample_acc
                
                avg_loss = total_loss / (batch_ind + 1)
                avg_ent_sample_acc = total_ent_sample_acc / (batch_ind + 1)
                avg_head_rel_sample_acc = total_head_rel_sample_acc / (batch_ind + 1)
                avg_tail_rel_sample_acc = total_tail_rel_sample_acc / (batch_ind + 1)

随着step加大，w\_ent的权重递减，w\_rel权重递增。先关注实体，保证实体抽准确，后面关注关系的抽取，由于目前工作原因，更多细节待闲时在进行解读。

百度信息抽取Lic2020关系抽取：[https://zhuanlan.zhihu.com/p/138858558][https_zhuanlan.zhihu.com_p_138858558]

[https_blog.csdn.net_weixin_42223207_article_details_116425447]: https://blog.csdn.net/weixin_42223207/article/details/116425447
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0YXlfZm9vbGlzaDEy_size_16_color_FFFFFF_t_70]: /images/20220829/016340c7ead549a9becdc34fb70a4ba8.png
[https_zhuanlan.zhihu.com_p_304104571]: https://zhuanlan.zhihu.com/p/304104571
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N0YXlfZm9vbGlzaDEy_size_16_color_FFFFFF_t_70 1]: /images/20220829/a46537bceb4e403ca6d2d0273f0dec75.png
[https_zhuanlan.zhihu.com_p_342300800]: https://zhuanlan.zhihu.com/p/342300800
[https_zhuanlan.zhihu.com_p_138858558]: https://zhuanlan.zhihu.com/p/138858558