保险文本视觉认知问答竞赛（Baseline）-人工智能-PHP中文网

本次赛题围绕保险扫描文档的OCR识别与智能问答展开，提供含票据等20多种类型的扫描文件数据集，含训练集5000余张图片及4万余个问答标注，测试集1000张左右图片及7000个问题。基线采用两阶段处理，先用PaddleOCR识别文本，再用PaddleNLP通过抽取式阅读理解得出答案，还给出了模型训练等相关内容及示例问答。

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保险文本视觉认知问答竞赛（baseline） - php中文网

赛题简介

在寿险、产险、健康险等保险的理赔流程和客户服务环节中，存在大量扫描文档，例如医疗票据、费用清单、病例等。
对这些扫描文档进行文字检测与识别，并且提取出结构化信息，可以用于极速理赔、个人健康管理等业务场景。
本次赛题将提供扫描文件数据集，参赛队利用OCR技术自动识别影像资料后，再通过AI智能判断所识别文字的内在逻辑，回答关于图片的自然语言问题。
问题的答案是可以从图片中提取的任何文本/标记。

数据简介

本次大赛提供的数据集使用的扫描文件类型包括票据、说明、报告等20 多种。混合了印刷、打字和手写的内容。
训练集有5000余张左右原始扫描文件及对应的 4万余个自然语言问答标注。提供的数据均已做了标注及脱敏。
训练集数据包括：
- image：包含所有原始扫描文件图像
- train.csv：问答训练库，包含序号（index）、问题 ID(quesiton_id)、图片名称(filename)、问题（question_text)、答案(answer_text)，共 5 列
- readme：数据说明文档
测试集数据规模为1000张左右原始扫描文件及对应的7000个自然语言问题，数据内容样例同训练集。
测试集包含以下3个文件：
- image：包含所有原始扫描文件图像
- test1.csv：问答测试库，包含序号（index）、问题 ID(quesiton_id)、图片路径(filename)、问题（question_text)，共 4 列
- readme：数据说明文档

数据样例

样例一：
- 提问：西药费的金额是多少？回答：140.16 提问：140.16元购买了什么药品？回答：{甲}缘沙坦胶囊{基}
样例二：

超能文献
超能文献是一款革命性的AI驱动医学文献搜索引擎。

105

查看详情
- 提问：这是一份关于什么药品的说明？回答：十三味疏肝胶囊提问：药品的有效期是多久？回答：1.5年

基线总体架构

基线项目使用的是两阶段的处理方式：
- PaddleOCR：图像 ==OCR==> 文本信息
- PaddleNLP：文本信息 + 问题 ==抽取式阅读理解==> 答案

安装依赖包

PaddleOCR：GitHub、Gitee
PaddleNLP：GitHub、Gitee

In [ ]

!pip install paddleocr==2.0.4 paddlenlp==2.0.0rc18

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解压官方数据集

如果需要自行处理数据，可以使用下方的命令进行解压

In [ ]

# !tar -xf data/data83016/dataset.tar -C data

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数据集预处理

使用 PaddleOCR 识别图片中的文本信息，将其作为抽取式阅读理解的文章
并将数据格式转换为 SQuAD 格式，方便后续 PaddleNLP 读取使用
处理完成的数据集以存放于 AIStudio 数据集中，可以直接挂载使用
如果需要重新生成数据集的话，可以运行下面的命令，处理需耗时一个半小时左右，请耐心等待

In [ ]

# !python gen_dataset.py

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模型训练

基于 PaddleNLP 开发的抽取式阅读理解
更多详细介绍请参考：『NLP经典项目集』08: 使用预训练模型完成阅读理解

In [1]

!mkdir /home/aistudio/checkpointimport paddleimport paddlenlp as ppnlpfrom functools import partialfrom paddlenlp.data import Stack, Dict, Padfrom utils import prepare_train_features, prepare_validation_features, evaluate############参数配置################ 模型名称MODEL_NAME = "ernie-1.0"# 最大文本长度max_seq_length = 512# 文本滑动窗口步幅doc_stride = 128# 训练过程中的最大学习率learning_rate = 3e-5 # 训练轮次epochs = 1# 数据批次大小batch_size = 8# 学习率预热比例warmup_proportion = 0.1# 权重衰减系数，类似模型正则项策略，避免模型过拟合weight_decay = 0.01#############模型################# 加载模型model = ppnlp.transformers.ErnieForQuestionAnswering.from_pretrained(MODEL_NAME)# 加载 tokenizertokenizer = ppnlp.transformers.ErnieTokenizer.from_pretrained(MODEL_NAME)#############数据################ 加载数据集train_ds = ppnlp.datasets.load_dataset('dureader_robust', data_files='data/data83268/train.json')
dev_ds = ppnlp.datasets.load_dataset('dureader_robust', data_files='data/data83268/dev.json')# 数据滑窗处理train_trans_func = partial(prepare_train_features, 
                           max_seq_length=max_seq_length, 
                           doc_stride=doc_stride,
                           tokenizer=tokenizer)

train_ds.map(train_trans_func, batched=True)

dev_trans_func = partial(prepare_validation_features, 
                           max_seq_length=max_seq_length, 
                           doc_stride=doc_stride,
                           tokenizer=tokenizer)
                           
dev_ds.map(dev_trans_func, batched=True)# 数据读取器配置train_batch_sampler = paddle.io.DistributedBatchSampler(
        train_ds, batch_size=batch_size, shuffle=True)

train_batchify_fn = lambda samples, fn=Dict({    "input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),    "token_type_ids": Pad(axis=0, pad_val=tokenizer.pad_token_type_id),    "start_positions": Stack(dtype="int64"),    "end_positions": Stack(dtype="int64")
}): fn(samples)

train_data_loader = paddle.io.DataLoader(
    dataset=train_ds,
    batch_sampler=train_batch_sampler,
    collate_fn=train_batchify_fn,
    return_list=True)

dev_batch_sampler = paddle.io.BatchSampler(
    dev_ds, batch_size=batch_size, shuffle=False)

dev_batchify_fn = lambda samples, fn=Dict({    "input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),    "token_type_ids": Pad(axis=0, pad_val=tokenizer.pad_token_type_id)
}): fn(samples)

dev_data_loader = paddle.io.DataLoader(
    dataset=dev_ds,
    batch_sampler=dev_batch_sampler,
    collate_fn=dev_batchify_fn,
    return_list=True)#############优化器配置############## 学习率策略num_training_steps = len(train_data_loader) * epochs
lr_scheduler = ppnlp.transformers.LinearDecayWithWarmup(learning_rate, num_training_steps, warmup_proportion)# Generate parameter names needed to perform weight decay.# All bias and LayerNorm parameters are excluded.decay_params = [
    p.name for n, p in model.named_parameters()    if not any(nd in n for nd in ["bias", "norm"])
]# 设置优化器optimizer = paddle.optimizer.AdamW(
    learning_rate=lr_scheduler,
    parameters=model.parameters(),
    weight_decay=weight_decay,
    apply_decay_param_fun=lambda x: x in decay_params)#############损失函数################class CrossEntropyLossForSQuAD(paddle.nn.Layer):
    def __init__(self):
        super(CrossEntropyLossForSQuAD, self).__init__()    def forward(self, y, label):
        start_logits, end_logits = y   # both shape are [batch_size, seq_len]
        start_position, end_position = label
        start_position = paddle.unsqueeze(start_position, axis=-1)
        end_position = paddle.unsqueeze(end_position, axis=-1)
        start_loss = paddle.nn.functional.softmax_with_cross_entropy(
            logits=start_logits, label=start_position, soft_label=False)
        start_loss = paddle.mean(start_loss)
        end_loss = paddle.nn.functional.softmax_with_cross_entropy(
            logits=end_logits, label=end_position, soft_label=False)
        end_loss = paddle.mean(end_loss)

        loss = (start_loss + end_loss) / 2
        return loss#############模型训练################# 实例化 losscriterion = CrossEntropyLossForSQuAD()
global_step = 0# 训练for epoch in range(1, epochs + 1):    for step, batch in enumerate(train_data_loader, start=1):
        global_step += 1
        input_ids, segment_ids, start_positions, end_positions = batch
        logits = model(input_ids=input_ids, token_type_ids=segment_ids)
        loss = criterion(logits, (start_positions, end_positions))        if global_step % 100 == 0 :            print("global step %d, epoch: %d, batch: %d, loss: %.5f" % (global_step, epoch, step, loss))
        loss.backward()
        optimizer.step()
        lr_scheduler.step()
        optimizer.clear_grad()

    evaluate(model=model, data_loader=dev_data_loader) 

# 保存model.save_pretrained('/home/aistudio/checkpoint')
tokenizer.save_pretrained('/home/aistudio/checkpoint')

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[2021-04-22 20:48:38,873] [    INFO] - Already cached /home/aistudio/.paddlenlp/models/ernie-1.0/ernie_v1_chn_base.pdparams
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1303: UserWarning: Skip loading for classifier.weight. classifier.weight is not found in the provided dict.
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1303: UserWarning: Skip loading for classifier.bias. classifier.bias is not found in the provided dict.
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
[2021-04-22 20:48:42,972] [    INFO] - Found /home/aistudio/.paddlenlp/models/ernie-1.0/vocab.txt

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global step 100, epoch: 1, batch: 100, loss: 5.33133
global step 200, epoch: 1, batch: 200, loss: 2.81528
global step 300, epoch: 1, batch: 300, loss: 1.96900
global step 400, epoch: 1, batch: 400, loss: 1.99122
global step 500, epoch: 1, batch: 500, loss: 2.26535
global step 600, epoch: 1, batch: 600, loss: 1.91357
global step 700, epoch: 1, batch: 700, loss: 1.60655
global step 800, epoch: 1, batch: 800, loss: 1.76000
global step 900, epoch: 1, batch: 900, loss: 1.13124
global step 1000, epoch: 1, batch: 1000, loss: 1.72126
global step 1100, epoch: 1, batch: 1100, loss: 1.89857
global step 1200, epoch: 1, batch: 1200, loss: 1.47760
global step 1300, epoch: 1, batch: 1300, loss: 1.70778
global step 1400, epoch: 1, batch: 1400, loss: 1.30835
global step 1500, epoch: 1, batch: 1500, loss: 2.72890
global step 1600, epoch: 1, batch: 1600, loss: 1.84454
global step 1700, epoch: 1, batch: 1700, loss: 3.09311
global step 1800, epoch: 1, batch: 1800, loss: 1.83173
global step 1900, epoch: 1, batch: 1900, loss: 1.07240
global step 2000, epoch: 1, batch: 2000, loss: 1.33060
global step 2100, epoch: 1, batch: 2100, loss: 1.04376
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global step 2300, epoch: 1, batch: 2300, loss: 2.03573
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global step 2500, epoch: 1, batch: 2500, loss: 1.08369
global step 2600, epoch: 1, batch: 2600, loss: 1.38365
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global step 2800, epoch: 1, batch: 2800, loss: 1.26852
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global step 3000, epoch: 1, batch: 3000, loss: 1.91332
global step 3100, epoch: 1, batch: 3100, loss: 1.47257
global step 3200, epoch: 1, batch: 3200, loss: 1.06718
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global step 4000, epoch: 1, batch: 4000, loss: 0.91027
global step 4100, epoch: 1, batch: 4100, loss: 0.81818
global step 4200, epoch: 1, batch: 4200, loss: 1.16851
global step 4300, epoch: 1, batch: 4300, loss: 1.64349
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global step 5200, epoch: 1, batch: 5200, loss: 0.97293
global step 5300, epoch: 1, batch: 5300, loss: 1.39240
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global step 9000, epoch: 1, batch: 9000, loss: 1.29235
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global step 12200, epoch: 1, batch: 12200, loss: 1.91975
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global step 12400, epoch: 1, batch: 12400, loss: 1.47376
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global step 15500, epoch: 1, batch: 15500, loss: 1.28670
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global step 16400, epoch: 1, batch: 16400, loss: 0.92708
global step 16500, epoch: 1, batch: 16500, loss: 0.95624
global step 16600, epoch: 1, batch: 16600, loss: 1.29848
global step 16700, epoch: 1, batch: 16700, loss: 1.27211
global step 16800, epoch: 1, batch: 16800, loss: 1.17851
global step 16900, epoch: 1, batch: 16900, loss: 1.28291
global step 17000, epoch: 1, batch: 17000, loss: 1.08720
global step 17100, epoch: 1, batch: 17100, loss: 1.08356
global step 17200, epoch: 1, batch: 17200, loss: 1.00867
Processing example: 1000
time per 1000: 11.201786994934082
Processing example: 2000
time per 1000: 11.235816478729248
Processing example: 3000
time per 1000: 10.834845066070557
Processing example: 4000
time per 1000: 11.04150128364563
Processing example: 5000
time per 1000: 11.004519701004028
Processing example: 6000
time per 1000: 11.003149509429932
Processing example: 7000
time per 1000: 11.149619340896606
{
  "exact": 56.03663613655287,
  "f1": 72.53400335174827,
  "total": 1201,
  "HasAns_exact": 56.03663613655287,
  "HasAns_f1": 72.53400335174827,
  "HasAns_total": 1201
}

问题： 本次医保范围支付多少钱？
原文： 54020292北京市医疗网珍收费票据医保已世结發部监NO财16139-54-02实时结算：★医疗机构类型：交易流水号：2411000107180415993045社会保障卡号40096415918041502915城镇工男医保类型：单价数量单位业务流水号：性别：15380等级项目／规格姓名：金额有自作数量/单位鸡7500单价中成药贸6.2Y项目规格无自付：复方甲氧那明胶/48粒23.75001/瓶12.E200西药费收都联153.8000付jia酸左氧沙星/0.116.2G00无苏黄止咳囊/Q.45g2粒76.90002/津有效遣夫不北京市财政局印制·20172收费专用道172.32自付一17232000172.32起村金额17.750.G0衣饮医保范内金狮1332.51封顶金额0.00门诊大额支付0.0自付二0.累计医供内范金额190.07退体补充支付0.00年门诊大额票计支付0.白费个人支付金额陵军补财支付0.00190.070.09本饮支付后·个人账户余额单位补充险[原公疗]支付个人账户支付0.00基金支情2合计（大写收款人收款单位（章）
答案： 172.32

问题： 9260是什么的编号？
原文： 54020292北京市医疗网珍收费票据医保已世结發部监NO财16139-54-02实时结算：★医疗机构类型：交易流水号：2411000107180415993045社会保障卡号40096415918041502915城镇工男医保类型：单价数量单位业务流水号：性别：15380等级项目／规格姓名：金额有自作数量/单位鸡7500单价中成药贸6.2Y项目规格无自付：复方甲氧那明胶/48粒23.75001/瓶12.E200西药费收都联153.8000付jia酸左氧沙星/0.116.2G00无苏黄止咳囊/Q.45g2粒76.90002/津有效遣夫不北京市财政局印制·20172收费专用道172.32自付一17232000172.32起村金额17.750.G0衣饮医保范内金狮1332.51封顶金额0.00门诊大额支付0.0自付二0.累计医供内范金额190.07退体补充支付0.00年门诊大额票计支付0.白费个人支付金额陵军补财支付0.00190.070.09本饮支付后·个人账户余额单位补充险[原公疗]支付个人账户支付0.00基金支情2合计（大写收款人收款单位（章）
答案： 收款单位

问题： 图7是表达什么的？
原文： 东莞证券DONGGUANSECURITIES盛达资源（000603）深度报告45720062003.5520042002.53200222001.5112000.52002008-01-022013-11-022015-01-022015-08-022016-03-022016-10-022017-05-022017-12-022018-07-022019-02-022008-08-022010-12-022012-02-022012-09-022013-04-022014-06-022019-09-022009-10-022010-05-022011-07-022009-03-022020-04-022222SS30202Q90010S20C20美国：所有联储银行：资产：总资产美国：国债收益率：10年美国：联邦基金利率（日）美国：所有联储银行：资产：持有证券：美国国债资料来源：wind，东莞证券研究所资料来源：wind，东莞证券研究所图7：美国国债总额迅速增加（十亿美元）图8：美元流动性危机解除280005.0025.00260004.50240004.0020.00220003.503.002000015.002.50180002.001600010.00140001.501.00120005.00100000.5080000.002012-02-022013-11-022015-01-022016-03-022011-07-022013-04-022014-06-022015-08-022016-10-022017-05-022017-12-022008-01-022010-12-022012-09-022009-10-022010-05-022018-07-022008-08-022009-03-022019-02-022019-09-022012-09-022018-07-022008-01-022009-03-022009-10-022010-05-022010-12-022011-07-022012-02-022013-04-022013-11-022014-06-022015-08-022016-03-022017-05-022017-12-022019-02-022019-09-022020-04-022015-01-022016-10-022008-08-022020-04-02美国：国债总额-LIBOR：美元：3个月-美国：国债收益率：3个月M2同比增速（季调，右轴资料来源：wind，东莞证券研究所资料来源：wind，东莞证券研究所4.2.2美元处于下行通道，驱动黄金价格上行美国在疫情未受控情况下，强行重启经济，新冠新增感染人数仍在高位，市场对美国经济修复是否通畅存有忧虑。西欧及日本疫情率先于美国得到控制，美元相对欧元、英锈走弱。美国实施规模空前的财政刺激，导致政府负债迅速攀升，美元信用度下降美元指数下行将驱动以美元计价的黄金价格走高。图9：美国新冠新增感染人数仍在高位（人/日）图10：欧、日、美新冠新增感染人数（人/日）17请务必阅读末页声明。
答案： 美国国债总额迅速增加（十亿美元）

问题： 图8是说明什么的？
原文： 东莞证券DONGGUANSECURITIES盛达资源（000603）深度报告45720062003.5520042002.53200222001.5112000.52002008-01-022013-11-022015-01-022015-08-022016-03-022016-10-022017-05-022017-12-022018-07-022019-02-022008-08-022010-12-022012-02-022012-09-022013-04-022014-06-022019-09-022009-10-022010-05-022011-07-022009-03-022020-04-022222SS30202Q90010S20C20美国：所有联储银行：资产：总资产美国：国债收益率：10年美国：联邦基金利率（日）美国：所有联储银行：资产：持有证券：美国国债资料来源：wind，东莞证券研究所资料来源：wind，东莞证券研究所图7：美国国债总额迅速增加（十亿美元）图8：美元流动性危机解除280005.0025.00260004.50240004.0020.00220003.503.002000015.002.50180002.001600010.00140001.501.00120005.00100000.5080000.002012-02-022013-11-022015-01-022016-03-022011-07-022013-04-022014-06-022015-08-022016-10-022017-05-022017-12-022008-01-022010-12-022012-09-022009-10-022010-05-022018-07-022008-08-022009-03-022019-02-022019-09-022012-09-022018-07-022008-01-022009-03-022009-10-022010-05-022010-12-022011-07-022012-02-022013-04-022013-11-022014-06-022015-08-022016-03-022017-05-022017-12-022019-02-022019-09-022020-04-022015-01-022016-10-022008-08-022020-04-02美国：国债总额-LIBOR：美元：3个月-美国：国债收益率：3个月M2同比增速（季调，右轴资料来源：wind，东莞证券研究所资料来源：wind，东莞证券研究所4.2.2美元处于下行通道，驱动黄金价格上行美国在疫情未受控情况下，强行重启经济，新冠新增感染人数仍在高位，市场对美国经济修复是否通畅存有忧虑。西欧及日本疫情率先于美国得到控制，美元相对欧元、英锈走弱。美国实施规模空前的财政刺激，导致政府负债迅速攀升，美元信用度下降美元指数下行将驱动以美元计价的黄金价格走高。图9：美国新冠新增感染人数仍在高位（人/日）图10：欧、日、美新冠新增感染人数（人/日）17请务必阅读末页声明。
答案： 美元流动性危机解除

问题： 文中上面的四张图的资料来源都是哪里？
原文： 东莞证券DONGGUANSECURITIES盛达资源（000603）深度报告45720062003.5520042002.53200222001.5112000.52002008-01-022013-11-022015-01-022015-08-022016-03-022016-10-022017-05-022017-12-022018-07-022019-02-022008-08-022010-12-022012-02-022012-09-022013-04-022014-06-022019-09-022009-10-022010-05-022011-07-022009-03-022020-04-022222SS30202Q90010S20C20美国：所有联储银行：资产：总资产美国：国债收益率：10年美国：联邦基金利率（日）美国：所有联储银行：资产：持有证券：美国国债资料来源：wind，东莞证券研究所资料来源：wind，东莞证券研究所图7：美国国债总额迅速增加（十亿美元）图8：美元流动性危机解除280005.0025.00260004.50240004.0020.00220003.503.002000015.002.50180002.001600010.00140001.501.00120005.00100000.5080000.002012-02-022013-11-022015-01-022016-03-022011-07-022013-04-022014-06-022015-08-022016-10-022017-05-022017-12-022008-01-022010-12-022012-09-022009-10-022010-05-022018-07-022008-08-022009-03-022019-02-022019-09-022012-09-022018-07-022008-01-022009-03-022009-10-022010-05-022010-12-022011-07-022012-02-022013-04-022013-11-022014-06-022015-08-022016-03-022017-05-022017-12-022019-02-022019-09-022020-04-022015-01-022016-10-022008-08-022020-04-02美国：国债总额-LIBOR：美元：3个月-美国：国债收益率：3个月M2同比增速（季调，右轴资料来源：wind，东莞证券研究所资料来源：wind，东莞证券研究所4.2.2美元处于下行通道，驱动黄金价格上行美国在疫情未受控情况下，强行重启经济，新冠新增感染人数仍在高位，市场对美国经济修复是否通畅存有忧虑。西欧及日本疫情率先于美国得到控制，美元相对欧元、英锈走弱。美国实施规模空前的财政刺激，导致政府负债迅速攀升，美元信用度下降美元指数下行将驱动以美元计价的黄金价格走高。图9：美国新冠新增感染人数仍在高位（人/日）图10：欧、日、美新冠新增感染人数（人/日）17请务必阅读末页声明。
答案： wind，东莞证券研究所

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