paddlenlp模块下DatasetBuilder类如何加载本地自定义数据，请代码展示

加载本地自定义数据可以使用PaddleNLP中的DatasetBuilder类，具体步骤如下：

1. 创建一个继承自DatasetBuilder的自定义数据集类，并实现其中的process方法。

from paddlenlp.datasets import DatasetBuilder

class MyDataset(DatasetBuilder):
    def _get_data(self, mode, **kwargs):
        # mode参数指定是训练集还是测试集
        # kwargs可以用来传递其他参数
        pass

    def process(self, **kwargs):
        # 读取数据并进行预处理
        pass

2. 在process方法中读取数据并进行预处理，返回一个字典数据。

def process(self, **kwargs):
    data = []
    with open('data.txt', 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            label, text = line.split('\t')
            data.append({'label': label, 'text': text})
    return data

3. 在自定义数据集类中实现_build_vocab方法，用于构建词表。

def _build_vocab(self, examples):
    # 这里可以使用PaddleNLP提供的Vocab类进行词表构建
    pass

4. 使用自定义数据集类加载数据。

my_dataset = MyDataset()
train_ds = my_dataset.get_train_examples()
test_ds = my_dataset.get_test_examples()

完整代码如下：

from paddlenlp.datasets import DatasetBuilder, MapDataset
from paddlenlp.data import Stack, Tuple, Pad
from paddlenlp.utils.log import logger


class MyDataset(DatasetBuilder):
    def _get_data(self, mode, **kwargs):
        pass

    def process(self, **kwargs):
        data = []
        with open('data.txt', 'r', encoding='utf-8') as f:
            for line in f:
                line = line.strip()
                if not line:
                    continue
                label, text = line.split('\t')
                data.append({'label': label, 'text': text})
        return data

    def _build_vocab(self, examples):
        pass

    def get_labels(self):
        return ["0", "1"]


def convert_example(example,
                    tokenizer,
                    max_seq_length=512,
                    is_test=False):
    query, label = example['text'], example.get('label', None)
    tokenized_input = tokenizer(
        query,
        max_seq_len=max_seq_length,
        truncation=True,
        return_attention_mask=True,
        return_token_type_ids=False,
        return_special_tokens_mask=True)
    input_ids = tokenized_input['input_ids']
    attention_mask = tokenized_input['attention_mask']
    special_tokens_mask = tokenized_input['special_tokens_mask']
    if not is_test:
        return input_ids, attention_mask, special_tokens_mask, label
    else:
        return input_ids, attention_mask, special_tokens_mask


def create_dataloader(dataset,
                      mode='train',
                      batch_size=1,
                      batchify_fn=None,
                      trans_fn=None):
    if trans_fn:
        dataset = dataset.map(trans_fn)
    shuffle = True if mode == 'train' else False
    batch_sampler = paddle.io.DistributedBatchSampler(
        dataset, batch_size=batch_size, shuffle=shuffle)
    return paddle.io.DataLoader(
        dataset=dataset,
        batch_sampler=batch_sampler,
        collate_fn=batchify_fn,
        num_workers=0,
        return_list=True)


if __name__ == '__main__':
    # 加载自定义数据集
    my_dataset = MyDataset()
    train_ds = my_dataset.get_train_examples()

    # 构建词表
    tokenizer = paddlenlp.data.JiebaTokenizer()
    my_dataset._build_vocab(train_ds)

    # 定义数据处理函数和batchify函数
    trans_fn = lambda example: convert_example(example, tokenizer)
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input_ids
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # attention_mask
        Stack(),  # special_tokens_mask
        Stack(dtype='int64')  # label
    ): fn(samples)

    # 创建dataloader
    train_loader = create_dataloader(
        train_ds,
        mode='train',
        batch_size=32,
        batchify_fn=batchify_fn,
        trans_fn=trans_fn)
    for batch in train_loader:
        input_ids, attention_mask, special_tokens_mask, label = batch
        logger.info('input_ids shape: %s' % str(input_ids.shape))
        logger.info('attention_mask shape: %s' % str(attention_mask.shape))
        logger.info('special_tokens_mask shape: %s' %
                    str(special_tokens_mask.shape))
        logger.info('label shape: %s' % str(label.shape))
        break

其中，convert_example函数用于将样本转换为模型输入格式，create_dataloader函数用于创建dataloader。