自然语言处理 Paddle NLP - 基于预训练模型完成实体关系抽取

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自然语言处理 Paddle NLP - 信息抽取技术及应用

重点：SOP 图、BCEWithLogitsLoss

基于预训练模型完成实体关系抽取

信息抽取旨在从非结构化自然语言文本中提取结构化知识，如实体、关系、事件等。对于给定的自然语言句子，根据预先定义的schema集合，抽取出所有满足schema约束的SPO三元组。

例如，「妻子」关系的schema定义为：

{
    S_TYPE: 人物,
    P: 妻子,
    O_TYPE: {
        @value: 人物
    }
}

该示例展示了如何使用PaddleNLP快速完成实体关系抽取，参与千言信息抽取-关系抽取比赛打榜。

关系抽取介绍

针对 DuIE2.0 任务中多条、交叠SPO这一抽取目标，比赛对标准的 'BIO' 标注进行了扩展。
对于每个 token，根据其在实体span中的位置（包括B、I、O三种），我们为其打上三类标签，并且根据其所参与构建的predicate种类，将 B 标签进一步区分。给定 schema 集合，对于 N 种不同 predicate，以及头实体/尾实体两种情况，我们设计对应的共 2N 种 B 标签，再合并 I 和 O 标签，故每个 token 一共有 (2N+2) 个标签，如下图所示。

以类别为标注

评价方法

对测试集上参评系统输出的SPO结果和人工标注的SPO结果进行精准匹配，采用F1值作为评价指标。注意，对于复杂O值类型的SPO，必须所有槽位都精确匹配才认为该SPO抽取正确。针对部分文本中存在实体别名的问题，使用百度知识图谱的别名词典来辅助评测。F1值的计算方式如下：

F1 = (2 * P * R) / (P + R)，其中

• P = 测试集所有句子中预测正确的SPO个数 / 测试集所有句子中预测出的SPO个数
• R = 测试集所有句子中预测正确的SPO个数 / 测试集所有句子中人工标注的SPO个数

Step1：构建模型

该任务可以看作一个序列标注任务，所以基线模型采用的是ERNIE序列标注模型。

PaddleNLP提供了ERNIE预训练模型常用序列标注模型，可以通过指定模型名字完成一键加载。PaddleNLP为了方便用户处理数据，内置了对于各个预训练模型对应的Tokenizer，可以完成文本token化，转token ID，文本长度截断等操作。

文本数据处理直接调用tokenizer即可输出模型所需输入数据。

├── dev_data.json
├── dev.json
├── duie.json
├── duie.json.zip
├── duie_sample
│   └── License.docx
├── id2spo.json
├── predicate2id.json            # 有多少类型
├── schema.xlsx
├── test_data.json
├── test.json
├── train_data.json              # 训练数据
└── train.json

{
    "text":"《邪少兵王》是冰火未央写的网络小说连载于旗峰天下",           # 要抽取的一段话
    "spo_list":[                                                 # 标签结果（抽多少个三元组）
        {
            "predicate":"作者",                                   # 关系：作者
            "object_type":{
                "@value":"人物"                                   # 尾实体，是个人物
            },
            "subject_type":"图书作品",                             # 抽首实体是个 图书作品
            "object":{
                "@value":"冰火未央"                                # 尾实体人物的值：冰火未央
            },
            "subject":"邪少兵王"                                   # 图书作品的值 邪少兵王
        }
    ]
}

import os
import sys
import json
from paddlenlp.transformers import ErnieForTokenClassification, ErnieTokenizer

# 将 57 种关系标签读进来
label_map_path = os.path.join('/home/aistudio/relation_extraction/data', "predicate2id.json")

if not (os.path.exists(label_map_path) and os.path.isfile(label_map_path)):
    sys.exit("{} dose not exists or is not a file.".format(label_map_path))
with open(label_map_path, 'r', encoding='utf8') as fp:
    label_map = json.load(fp)

# 多标签分类的分类数： 57 - 2（I、O) * 2 （2种尾实体（value、inwork），所以在关系里面也要*2 两种关系）+ 2 （最后把 I、O加回来）
# 2N + 2
num_classes = (len(label_map.keys()) - 2) * 2 + 2

# 要做多标签分类问题，所以要把 num_classes 放到 pretrained 里，这边会用到 Sigmoid
model = ErnieForTokenClassification.from_pretrained("ernie-1.0", num_classes=(len(label_map) - 2) * 2 + 2)
tokenizer = ErnieTokenizer.from_pretrained("ernie-1.0")

#inputs = tokenizer(text="请输入测试样例", max_seq_len=20)
inputs = tokenizer(text="《邪少兵王》是冰火未央写的网络小说连载于旗峰天下", max_seq_len=20)
inputs

1 => CLS、后面是 token id
token_type_ids 全是0，因为只有一句话
{'input_ids': [1,  56,  1686,  332,  714,  338,  55,  10,  1161,  610,  556,  946,  519,  5,  305,  742,  96,  178,  538,  2],
 'token_type_ids': [0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0]}

Step2：加载并处理数据

从比赛官网下载数据集，解压存放于data/目录下并重命名为train_data.json, dev_data.json, test_data.json.

我们可以加载自定义数据集。通过继承paddle.io.Dataset，自定义实现__getitem__ 和 __len__两个方法。

from typing import Optional, List, Union, Dict

import numpy as np
import paddle
from tqdm import tqdm

from paddlenlp.transformers import ErnieTokenizer
from paddlenlp.utils.log import logger

from data_loader import parse_label, DataCollator, convert_example_to_feature
from extract_chinese_and_punct import ChineseAndPunctuationExtractor


class DuIEDataset(paddle.io.Dataset):
    def __init__(self, data, label_map, tokenizer, max_length=512, pad_to_max_length=False):
        super(DuIEDataset, self).__init__()

        self.data = data
        self.chn_punc_extractor = ChineseAndPunctuationExtractor()
        self.tokenizer = tokenizer
        self.max_seq_length = max_length
        self.pad_to_max_length = pad_to_max_length
        self.label_map = label_map

    def __len__(self):
        return len(self.data)

    def __getitem__(self, item):

        example = json.loads(self.data[item])
        input_feature = convert_example_to_feature(
            example, self.tokenizer, self.chn_punc_extractor,
            self.label_map, self.max_seq_length, self.pad_to_max_length)
        return {
            "input_ids": np.array(input_feature.input_ids, dtype="int64"),
            "seq_lens": np.array(input_feature.seq_len, dtype="int64"),
            "tok_to_orig_start_index":
            np.array(input_feature.tok_to_orig_start_index, dtype="int64"),
            "tok_to_orig_end_index": 
            np.array(input_feature.tok_to_orig_end_index, dtype="int64"),
            # If model inputs is generated in `collate_fn`, delete the data type casting.
            "labels": np.array(input_feature.labels, dtype="float32"),
        }


    @classmethod
    def from_file(cls,
                  file_path,
                  tokenizer,
                  max_length=512,
                  pad_to_max_length=None):
        assert os.path.exists(file_path) and os.path.isfile(
            file_path), f"{file_path} dose not exists or is not a file."
        label_map_path = os.path.join(
            os.path.dirname(file_path), "predicate2id.json")
        assert os.path.exists(label_map_path) and os.path.isfile(
            label_map_path
        ), f"{label_map_path} dose not exists or is not a file."
        with open(label_map_path, 'r', encoding='utf8') as fp:
            label_map = json.load(fp)

        with open(file_path, "r", encoding="utf-8") as fp:
            data = fp.readlines()
            return cls(data, label_map, tokenizer, max_length, pad_to_max_length)

data_path = 'data'
batch_size = 32
max_seq_length = 128

train_file_path = os.path.join(data_path, 'train_data.json')
train_dataset = DuIEDataset.from_file(
    train_file_path, tokenizer, max_seq_length, True)

# print(len(train_dataset))
# print(train_dataset[0])


train_batch_sampler = paddle.io.BatchSampler(
    train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
collator = DataCollator()
train_data_loader = paddle.io.DataLoader(
    dataset=train_dataset,
    batch_sampler=train_batch_sampler,
    collate_fn=collator)

eval_file_path = os.path.join(data_path, 'dev_data.json') # 防止内存溢出，这边用了 _data 结果的试验数据，dev.json 全量数据 17W+
test_dataset = DuIEDataset.from_file(
    eval_file_path, tokenizer, max_seq_length, True)
test_batch_sampler = paddle.io.BatchSampler(
    test_dataset, batch_size=batch_size, shuffle=False, drop_last=True)
test_data_loader = paddle.io.DataLoader(
    dataset=test_dataset,
    batch_sampler=test_batch_sampler,
    collate_fn=collator)

Step3：定义损失函数和优化器，开始训练

我们选择均方误差作为损失函数，使用paddle.optimizer.AdamW作为优化器。

在训练过程中，模型保存在当前目录checkpoints文件夹下。同时在训练的同时使用官方评测脚本进行评估，输出P/R/F1指标。
在验证集上F1可以达到69.42。

import paddle.nn as nn

# 多标签分类，BCEWithLogitsLoss
class BCELossForDuIE(nn.Layer):
    def __init__(self, ):
        super(BCELossForDuIE, self).__init__()
        self.criterion = nn.BCEWithLogitsLoss(reduction='none')

    def forward(self, logits, labels, mask):
        loss = self.criterion(logits, labels)
        mask = paddle.cast(mask, 'float32') # 有的标签是PAD的，不需要计算，减少 mask 计算量
        loss = loss * mask.unsqueeze(-1)
        loss = paddle.sum(loss.mean(axis=2), axis=1) / paddle.sum(mask, axis=1)
        loss = loss.mean()
        return loss

from utils import write_prediction_results, get_precision_recall_f1, decoding

@paddle.no_grad()
def evaluate(model, criterion, data_loader, file_path, mode):
    """
    mode eval:
    eval on development set and compute P/R/F1, called between training.
    mode predict:
    eval on development / test set, then write predictions to \
        predict_test.json and predict_test.json.zip \
        under /home/aistudio/relation_extraction/data dir for later submission or evaluation.
    """
    example_all = []
    with open(file_path, "r", encoding="utf-8") as fp:
        for line in fp:
            example_all.append(json.loads(line))
    # id2spo.json => {"predicate": ["empty", "empty", "注册资本"..}
    id2spo_path = os.path.join(os.path.dirname(file_path), "id2spo.json")
    with open(id2spo_path, 'r', encoding='utf8') as fp:
        id2spo = json.load(fp)

    model.eval()
    loss_all = 0
    eval_steps = 0
    formatted_outputs = []
    current_idx = 0
    for batch in tqdm(data_loader, total=len(data_loader)):
        eval_steps += 1
        input_ids, seq_len, tok_to_orig_start_index, tok_to_orig_end_index, labels = batch
        logits = model(input_ids=input_ids)
        mask = (input_ids != 0).logical_and((input_ids != 1)).logical_and((input_ids != 2))
        loss = criterion(logits, labels, mask)
        loss_all += loss.numpy().item()
        probs = F.sigmoid(logits)
        logits_batch = probs.numpy()
        seq_len_batch = seq_len.numpy()
        tok_to_orig_start_index_batch = tok_to_orig_start_index.numpy()
        tok_to_orig_end_index_batch = tok_to_orig_end_index.numpy()
        formatted_outputs.extend(decoding(example_all[current_idx: current_idx+len(logits)],
                                          id2spo,
                                          logits_batch,
                                          seq_len_batch,
                                          tok_to_orig_start_index_batch,
                                          tok_to_orig_end_index_batch))
        current_idx = current_idx+len(logits)
    loss_avg = loss_all / eval_steps
    print("eval loss: %f" % (loss_avg))

    if mode == "predict":
        predict_file_path = os.path.join("/home/aistudio/relation_extraction/data", 'predictions.json')
    else:
        predict_file_path = os.path.join("/home/aistudio/relation_extraction/data", 'predict_eval.json')

    predict_zipfile_path = write_prediction_results(formatted_outputs,
                                                    predict_file_path)

    if mode == "eval":
        precision, recall, f1 = get_precision_recall_f1(file_path,
                                                        predict_zipfile_path)
        os.system('rm {} {}'.format(predict_file_path, predict_zipfile_path))
        return precision, recall, f1
    elif mode != "predict":
        raise Exception("wrong mode for eval func")

from paddlenlp.transformers import LinearDecayWithWarmup

learning_rate = 2e-5
num_train_epochs = 5
warmup_ratio = 0.06

criterion = BCELossForDuIE()
# Defines learning rate strategy.
steps_by_epoch = len(train_data_loader)
num_training_steps = steps_by_epoch * num_train_epochs
lr_scheduler = LinearDecayWithWarmup(learning_rate, num_training_steps, warmup_ratio)
optimizer = paddle.optimizer.AdamW(
    learning_rate=lr_scheduler,
    parameters=model.parameters(),
    apply_decay_param_fun=lambda x: x in [
        p.name for n, p in model.named_parameters()
        if not any(nd in n for nd in ["bias", "norm"])])

# 模型参数保存路径
!mkdir checkpoints

import time
import paddle.nn.functional as F

# Starts training.
global_step = 0
logging_steps = 50
save_steps = 10000
num_train_epochs = 2
output_dir = 'checkpoints'
tic_train = time.time()
model.train()
for epoch in range(num_train_epochs):
    print("\n=====start training of %d epochs=====" % epoch)
    tic_epoch = time.time()
    for step, batch in enumerate(train_data_loader):
        input_ids, seq_lens, tok_to_orig_start_index, tok_to_orig_end_index, labels = batch
        logits = model(input_ids=input_ids)
        mask = (input_ids != 0).logical_and((input_ids != 1)).logical_and(
            (input_ids != 2))
        loss = criterion(logits, labels, mask)
        loss.backward()
        optimizer.step()
        lr_scheduler.step()
        optimizer.clear_gradients()
        loss_item = loss.numpy().item()

        if global_step % logging_steps == 0:
            print(
                "epoch: %d / %d, steps: %d / %d, loss: %f, speed: %.2f step/s"
                % (epoch, num_train_epochs, step, steps_by_epoch,
                    loss_item, logging_steps / (time.time() - tic_train)))
            tic_train = time.time()

        if global_step % save_steps == 0 and global_step != 0:
            print("\n=====start evaluating ckpt of %d steps=====" %
                    global_step)
            precision, recall, f1 = evaluate(
                model, criterion, test_data_loader, eval_file_path, "eval")
            print("precision: %.2f\t recall: %.2f\t f1: %.2f\t" %
                    (100 * precision, 100 * recall, 100 * f1))
            print("saving checkpoing model_%d.pdparams to %s " %
                    (global_step, output_dir))
            paddle.save(model.state_dict(),
                        os.path.join(output_dir, 
                                        "model_%d.pdparams" % global_step))
            model.train()

        global_step += 1
    tic_epoch = time.time() - tic_epoch
    print("epoch time footprint: %d hour %d min %d sec" %
            (tic_epoch // 3600, (tic_epoch % 3600) // 60, tic_epoch % 60))

# Does final evaluation.
print("\n=====start evaluating last ckpt of %d steps=====" %
        global_step)
precision, recall, f1 = evaluate(model, criterion, test_data_loader,
                                    eval_file_path, "eval")
print("precision: %.2f\t recall: %.2f\t f1: %.2f\t" %
        (100 * precision, 100 * recall, 100 * f1))
paddle.save(model.state_dict(),
            os.path.join(output_dir,
                            "model_%d.pdparams" % global_step))
print("\n=====training complete=====")

=====start training of 0 epochs=====
epoch: 0 / 2, steps: 0 / 5347, loss: 0.741724, speed: 66.93 step/s
epoch: 0 / 2, steps: 50 / 5347, loss: 0.733860, speed: 3.39 step/s
epoch: 0 / 2, steps: 100 / 5347, loss: 0.705046, speed: 3.35 step/s
epoch: 0 / 2, steps: 150 / 5347, loss: 0.633157, speed: 3.30 step/s
epoch: 0 / 2, steps: 200 / 5347, loss: 0.410678, speed: 3.24 step/s
epoch: 0 / 2, steps: 250 / 5347, loss: 0.302669, speed: 3.31 step/s
epoch: 0 / 2, steps: 300 / 5347, loss: 0.254647, speed: 3.29 step/s
epoch: 0 / 2, steps: 350 / 5347, loss: 0.224945, speed: 3.31 step/s
epoch: 0 / 2, steps: 400 / 5347, loss: 0.201895, speed: 3.26 step/s
epoch: 0 / 2, steps: 450 / 5347, loss: 0.179081, speed: 3.20 step/s
epoch: 0 / 2, steps: 500 / 5347, loss: 0.159897, speed: 3.30 step/s
......

Step4：提交预测结果
加载训练保存的模型加载后进行预测。

NOTE: 注意设置用于预测的模型参数路径。

set -eux

export CUDA_VISIBLE_DEVICES=0
export BATCH_SIZE=8
export CKPT=./checkpoints/model_624.pdparams
export DATASET_FILE=./data/test_data.json

python run_duie.py \
    --do_predict \
    --init_checkpoint $CKPT \
    --predict_data_file $DATASET_FILE \
    --max_seq_length 512 \
    --batch_size $BATCH_SIZE

!bash predict.sh

预测结果会被保存在data/predictions.json，data/predictions.json.zip，其格式与原数据集文件一致。

之后可以使用官方评估脚本评估训练模型在dev_data.json上的效果。如：

python re_official_evaluation.py --golden_file=dev_data.json  --predict_file=predicitons.json.zip [--alias_file alias_dict]

输出指标为Precision, Recall 和 F1，Alias file包含了合法的实体别名，最终评测的时候会使用，这里不予提供。

之后在test_data.json上预测，然后预测结果（submission.zip文件）至千言评测页面。

Tricks

尝试更多的预训练模型

基线采用的预训练模型为ERNIE，PaddleNLP提供了丰富的预训练模型，如BERT，RoBERTa，Electra，XLNet等
参考预训练模型文档

如可以选择RoBERTa large中文模型优化模型效果，只需更换模型和tokenizer即可无缝衔接。

from paddlenlp.transformers import RobertaForTokenClassification, RobertaTokenizer

model = RobertaForTokenClassification.from_pretrained(
    "roberta-wwm-ext-large",
    num_classes=(len(label_map) - 2) * 2 + 2)
tokenizer = RobertaTokenizer.from_pretrained("roberta-wwm-ext-large")

原文
https://aistudio.baidu.com/aistudio/projectdetail/1639963?sUid=2631487&shared=1&ts=1686032358184

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