引入了新的数据DuIE进行重新训练
使用脚本对数据的格式进行转化
对测试数据的转化见先前

import json
def find_all(sub, s):
    index_list = []
    index = s.find(sub)
    while index != -1:
        index_list.append([index,index+len(sub)-1])
        index = s.find(sub, index + 1)
    if len(index_list) > 0:
        return index_list
    else:
        #print("err")
        return index_list
#sen="生生不息CSOL生化狂潮让你填弹狂扫"
#print(find_all("CSOL",sen))
def func1():
    with open("D:\\New_desktop\\train_data.json", 'r', encoding='utf-8') as load_f:
        labels = {}
        info=[]
        error=0
        total=0
        l=['机构', '影视作品', '电视综艺', '网络小说', '歌曲',  '生物',  '历史人物',  '人物', '图书作品', '学校', '音乐专辑', '企业', '书籍',  '国家', '出版社', '目', '地点']
        for i in l:
            labels[i]=0
        import random
        for line in load_f.readlines():
            total+=1
            dic = json.loads(line)
            after={}
            after['text']=dic['text']
            after['label'] = {}
            append_flag=1
            for j in dic['spo_list']:
                if j['object_type'] in l:
                    labels[j['object_type']]+=1
                    after['label'][j['object_type']] = {}
                    result1 = find_all(j['object'], dic['text'])
                    if len(result1) != 0:
                        after['label'][j['object_type']][j['object']] = find_all(j['object'], dic['text'])
                    else:
                        append_flag = 0
                else:
                    append_flag=0
                if j['subject_type'] in l:
                    labels[j['subject_type']] += 1
                    after['label'][j['subject_type']]={}
                    result2=find_all(j['subject'], dic['text'])
                    if (len(result2))!=0:
                        after['label'][j['subject_type']][j['subject']] =find_all(j['subject'], dic['text'])
                    else:
                        append_flag=0
                else:
                    append_flag=0
            if append_flag:
                info.append(after)
        sub_train = []
        for i in range(20000):
            sub_train.append(random.choice(info))
        print(len(info),total)
    with open("D:\\New_desktop\\train.json", "w",encoding='utf-8') as dump_f:
        for i in sub_train:
            a = json.dumps(i, ensure_ascii=False)
            dump_f.write(a)
            dump_f.write("\n")
    with open("D:\\New_desktop\\dev_data.json", 'r', encoding='utf-8') as load_f:
        labels2 = {}
        for i in l:
            labels2[i] = 0
        info=[]
        error=0
        total=0
        for line in load_f.readlines():
            total += 1
            dic = json.loads(line)
            after = {}
            after['text'] = dic['text']
            append_flag = 1
            after['label'] = {}
            for j in dic['spo_list']:
                if j['object_type'] in l:
                    labels2[j['object_type']] += 1
                    after['label'][j['object_type']] = {}
                    result1 = find_all(j['object'], dic['text'])
                    if len(result1) != 0:
                        after['label'][j['object_type']][j['object']] = find_all(j['object'], dic['text'])
                    else:
                        append_flag = 0
                else:
                    append_flag = 0
                if j['subject_type'] in l:
                    labels2[j['subject_type']] += 1
                    after['label'][j['subject_type']] = {}
                    result2 = find_all(j['subject'], dic['text'])
                    if (len(result2)) != 0:
                        after['label'][j['subject_type']][j['subject']] = find_all(j['subject'], dic['text'])
                    else:
                        append_flag = 0
                else:
                    append_flag = 0
            if append_flag:
                info.append(after)
        print(len(info),total)
        for i in labels:
            print(i, labels[i],labels2[i])
    sub_info=[]
    for i in range(2000):
        sub_info.append(random.choice(info))
    with open("D:\\New_desktop\\dev.json", "w",encoding='utf-8') as dump_f:
        for i in sub_info:
            a = json.dumps(i, ensure_ascii=False)
            dump_f.write(a)
            dump_f.write("\n")
func1()

数据特性

共计28类的数据
观察到部分的类别的数量极度的不平衡
对应的结果发现有很大的偏差，或许是因为这个原因

解决方案

对应的对类别进行了裁剪
对应的多分类问题里，类别过多以及类别的数据量差异极大的影响了算法性能，因此对数据进行修剪得到新数据

• ‘机构’,
• ‘影视作品’,
• ‘电视综艺’,
• 网络小说’,
• ‘歌曲’,
• ‘生物’,
• 历史人物’,
• 人物’,
• ‘图书作品’,
• ‘学校’, ‘
• 音乐专辑’,
• ‘企业’,
• ‘书籍’,
• ‘国家’,
• ‘出版社’,
• ‘目’,
• 地点’

同时由于先前的数据量过大（17w+ 的Train以及 2w+的dev）
进行缩小至2w的train以及2k的dev
随后得到数据

实际测试