使用tokenizer的encode_plus方法,你可以轻松实现这些策略。例如,以下代码演示了如何进行截断: from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') text = "This is a very long text that needs to be truncated..." encoded_input = tokenizer.encode_plus( tex...
tokenizer的目的是为了分词,encode对分词后的每个单词进行编码 encode与encoder的区别: encode仅返回input_ids encoder返回: input_ids:输入的编号,101代表[cls],102代表[sep] token_type_ids:单词属于哪个句子,第一个句子为0,第二句子为1 attention_mask:需要对哪些单词做self_attention发布...
I see that from version 2.4.0 I was able to use encode_plus() with BertTokenizer However it seems like that is not the case anymore. AttributeError: 'BertTokenizer' object has no attribute 'encoder_plus' Is there a replacement to encode_...
#['[CLS]', '人', '工', '智', '能', '是', '计', '算', '机', '科', '学', '的', '一', '个', '分', '支', '。', '[SEP]'] 从运行结果可以看到encode确实在首尾增加了特殊词元[cls]和[sep]也就是1和2 encode_plus 返回更多相关信息: ids = tokenizer.encode_plus(sents...
encode_dict = tokenizer.encode_plus(text=tokens, max_length=256, pad_to_max_length=True, is_pretokenized=True, return_token_type_ids=True, return_attention_mask=True) tokens = ['[CLS]'] + tokens + ['[SEP]']print(' '.join(tokens))print(encode_dict['input_ids']) ...
encode_plus(text=tokens_a, text_pair=tokens_b, max_length=20, pad_to_max_length=True, truncation_strategy='only_second', is_pretokenized=True, return_token_type_ids=True, return_attention_mask=True) tokens = " ".join(['[CLS]'] + tokens_a + ['[SEP]'] + tokens_b + ['[SEP]...
encoded_text = tokenizer.encode_plus(text, max_length=max_length, padding=padding, truncation=True, return_tensors="pt") ``` 这里我们将 "This is a sample text." 转换为了 [101, 2023, 2003, 1037, 7099, 3793, 1012, 102, 0, 0]。其中,101 表示 [CLS],102 表示 [SEP],0 表示 padding...
encode_dict = tokenizer.encode_plus(text=tokens_a,text_pair=tokens_b,max_length=20,pad_to_max_length=True,truncation_strategy='only_second',is_pretokenized=True,return_token_type_ids=True,return_attention_mask=True)tokens = " ".join(['[CLS]'] + tokens_a + ['[SEP]'] + tokens_b +...
Hello, I installed and unistalled pytorch_pretrained_bert package couple of time via pip install and than by .whl files. But its always giving me this error. ThanksMember LysandreJik commented Oct 25, 2021 Hello! You should install transformers, not pytorch_pretrained_bert LysandreJik closed...
[动手写bert系列] 01 huggingface tokenizer (vocab,encode,decode)原理及细节 28:45 [动手写 bert 系列] 02 tokenizer encode_plus, token_type_ids(mlm,nsp) 15:05 [动手写 bert 系列] bert model architecture 模型架构初探(embedding + encoder + pooler) 19:01 [动手写 bert 系列] torch.no_grad...