import torchtext from torchtext.datasets import AG_NEWS from torchtext.data.utils import get_tokenizer # 定义分词器 tokenizer = get_tokenizer("basic_english") print(f"tokenizer: {tokenizer}") # 下载并加载数据集 train_iter = AG_NEWS(split='train') # 打印存储样本 texts = [] for (label, te...
torchtext支持的分词器 torchtext是pytorch自带的关于文本的处理工具。 torchtext支持的分词器 from torchtext.data.utilsimportget_tokenizertokenizer=get_tokenizer('basic_english') 在/Users/xuehuiping/anaconda3/envs/my_transformer/lib/python3.7/site-packages/torchtext/data/utils.py查看get_tokenizer的定义: defge...
from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator tokenizer = get_tokenizer('basic_english') train_iter = AG_NEWS(split='train') def yield_tokens(data_iter): for _, text in data_iter: yield tokenizer(text) vocab = build_vocab_from_iterator...
torchtext支持的分词器 from torchtext.data.utils import get_tokenizer tokenizer = get_tokenizer('basic_english') 1. 2. 3. 在/Users/xuehuiping/anaconda3/envs/my_transformer/lib/python3.7/site-packages/torchtext/data/utils.py查看get_tokenizer的定义: def get_tokenizer(tokenizer, language='en') 1....
train_data, test_data = IMDB.splits() ``` -分词:在加载数据后,需要对文本进行分词操作,将句子划分为单词或者字符。可以使用`torchtext.data.utils.get_tokenizer`函数来创建一个分词器。 ```python from torchtext.data.utils import get_tokenizer tokenizer = get_tokenizer('basic_english') train_data =...
datasets import text_classificationimport osimport torch.nn as nnimport torch.nn.functional as Ffrom torch.utils.data import DataLoaderimport timefrom torch.utils.data.dataset import random_splitimport refrom torchtext.data.utils import ngrams_iteratorfrom torchtext.data.utils import get_tokenizer在下...
importrefromtorchtext.data.utilsimportngrams_iteratorfromtorchtext.data.utilsimportget_tokenizer ag_news_label= {1 :"World",2 :"Sports",3 :"Business",4 :"Sci/Tec"}defpredict(text, model, vocab, ngrams): tokenizer= get_tokenizer("basic_english") ...
import re from torchtext.data.utils import ngrams_iterator from torchtext.data.utils import get_tokenizer 在下一步中,我们将定义ngrams和batch大小。ngrams特征用于捕获有关本地语序的重要信息。 我们使用bigram,数据集中的示例文本将是单个单词加上bigrams字符串的列表。
from torchtext.data.utils import ngrams_iterator from torchtext.data.utils import get_tokenizer 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 在下一步中,我们将定义ngrams和batch大小。ngrams特征用于捕获有关本地语序的重要信息。 我们使用bigram,数据集中的示例文本将是单个单词加上bigrams字符串的...
importtorchtextfromtorchtext.data.utilsimportget_tokenizer TEXT=torchtext.data.Field(tokenize=get_tokenizer("basic_english"),init_token='<sos>',eos_token='<eos>',lower=True)train_txt,val_txt,test_txt=torchtext.datasets.WikiText2.splits(TEXT)TEXT.build_vocab(train_txt)device=torch.device("cuda...