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。 NGRAMS = 2 BATCH_SIZE = 16 现在,我们将读取T...
tokenizer = get_tokenizer("basic_english")withtorch.no_grad(): text = torch.tensor([vocab[token]fortokeninngrams_iterator(tokenizer(text), ngrams)]) output = model(text, torch.tensor([0]))returnoutput.argmax(1).item() +1vocab = train_dataset.get_vocab() model = model.to("cpu") ...
7: 'NaturalPlace', 8: 'Village', 9: 'Animal', 10: 'Plant', 11: 'Album', 12: 'Film', 13: 'WrittenWork'} def predict(text, model, vocab, ngrams): tokenizer = get_tokenizer("basic_english") with torch.no
return [ x.text for x in NLP.tokenizer(text) if x.text != " " and len(x.text)>1] #有的同学tokenize1用不了,可以使用tokenize2。 def tokenize2(text): text = re.sub(r"\s", " ", text) if (len(text) > MAX_CHARS): text = text[:MAX_CHARS] return [w for w in text.split...
x.textforxinNLP.tokenizer(text)ifx.text !=" "andlen(x.text)>1]#有的同学tokenize1用不了,可以使用tokenize2。deftokenize2(text): text = re.sub(r"s"," ", text)if(len(text) > MAX_CHARS): text = text[:MAX_CHARS]return[wforwintext.split(' ')iflen(w)>1]#中文的分类器比较简单de...
return [ 15. x.text for x in NLP.tokenizer(text) if x.text != " " and len(x.text)>1] 16. 17. #有的同学 tokenize1 用不了,可以使用 tokenize2。 18. def tokenize2(text): 19. text = re.sub(r"\s", " ", text) 20. if (len(text) > MAX_CHARS): 21. text = text[:...
开源项目-go-web-tokenizer.zip 开源项目-go-web-tokenizer.zip,go-web/tokenizer - URL safe timed tokens 上传者:weixin_38744435时间:2019-09-05 CRNN.zip(win10可用) 此代码是CRNN模型代码,win10可以运行,本人近期有用过,有需要的可以下载(GitHub不会用),上传给有需要的朋友使用; ...
def tokenizer(text): return [word for word in jieba.lcut(text) if word.strip()] # 定义语料字段 TEXT = data.Field(sequential=True, tokenizer=tokenizer, fix_length=5) LABEL = data.Field(sequential=False, use_vocab=True) # 还需告诉fields处理哪些数据 ...