本文主要是介绍pytorch文本任务pipeline，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

文章目录

• 1. 读取文本数据制作成pytorch专用数据集
• 2. 对句子进行分词得到词库
• 3. 构建DataLoadar

1. 读取文本数据制作成pytorch专用数据集

以读取CSV数据为例，里面每行的数据格式为：label,text。下面的代码会得到一个IterableDataset类型对象，可用于后面的流程

import io
from torchtext.utils import unicode_csv_reader
from torchtext.data.datasets_utils import _RawTextIterableDataset
def _create_data_from_csv(data_path):
    with io.open(data_path, encoding="utf8") as f:
        reader = unicode_csv_reader(f)
        for row in reader:
            yield int(row[0]), ' '.join(row[1:])
def _create_raw_text_iterable_dataset(description=None,path=None):
    iterator=_create_data_from_csv(path)
    length=sum(1 for _ in iterator)
    iterator=_create_data_from_csv(path)
#    print("lenght: ",length)
    return _RawTextIterableDataset(description=description,full_num_lines=length,iterator=iterator)
train_iter = _create_raw_text_iterable_dataset(description="AG_NEWS",path="../dataset/AG_NEWS/train.csv")#

2. 对句子进行分词得到词库

from torchtext.data import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
tokenizer = get_tokenizer(tokenizer='basic_english', language='en')
#输入是字符串，输出是单词列表
tokens = tokenizer("You can now install TorchText using pip!")
print(tokens)
def yield_tokens(data_iter):
    for _, text in data_iter:
        yield tokenizer(text)

vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])

注意，参数tokenizer可以是其他分词函数。如果是None，则直接按空格切分（无法剔除紧挨着单词的标点符号）；如果是basic_english则会把标点符号单独拎出来，称为normalize

voc的常用属性/方法

1. len() → int： 返回词库大小
2. getitem(token: str) → int: 给定token返回其下标
3. set_default_index(index: Optional[int]) → None：用于设置OOV对应的词和下标

3. 构建DataLoadar

# In[]
#构建两个比较有用的lambda表达式
text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1
print(text_pipeline('here is the an example'))
print(label_pipeline('10'))
from torch.utils.data import DataLoader
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def collate_batch(batch):
    label_list, text_list, offsets = [], [], [0]
    for (_label, _text) in batch:
         label_list.append(label_pipeline(_label))
         processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)
         text_list.append(processed_text)
         offsets.append(processed_text.size(0))
    label_list = torch.tensor(label_list, dtype=torch.int64)
    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
    text_list = torch.cat(text_list)
    return label_list.to(device), text_list.to(device), offsets.to(device)
dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)

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