好吧,从结果看,有input_ids和attention_mask分别是分词后的token id和掩码。让我们使用分词器的函数convert_ids_to_tokens()将token id转换为对应的token,如下。 tokenizer.convert_ids_to_tokens(inputs.input_ids)['▁I','▁','loved','▁reading','▁the','▁Hung','er','▁Games',''] 由上可知,...
2.tokenizer.convert_tokens_to_ids 将token转化为对应的token index; 3. tokenizer.encode tokenize+convert_token_to_ids的复合版本,针对单句和句子对进行分词和转token ids,同时能够实现padding truncatation ,增加special token等功能 encode(text: Union[str, List[str], List[int]], text_pair: Union[str, ...
convert_tokens_to_ids() 代码语言:javascript 复制 ids = tokenizer.convert_tokens_to_ids(tokens) ids 输出: 代码语言:javascript 复制 [2052, 1110, 170, 1363, 1285, 1106, 3858, 11303, 1468] decode 代码语言:javascript 复制 print(tokenizer.decode([1468])) print(tokenizer.decode(ids)) # 注意...
原来embedding weight 不会变# 使用新的嵌入向量初始化新词汇的嵌入向量a = model.get_input_embeddings()print(a)# Embedding(30524, 768)tok = tokenizer.convert_tokens_to_ids(["newword"])print(tok)# [30522]# 保存微调后的模型和tokenizer(重要)model.save_pretrained("./gaibian") tokenizer.save_pre...
I am using Deberta Tokenizer. convert_ids_to_tokens() of the tokenizer is not working fine. The problem arises when using: my own modified scripts: (give details below) The tasks I am working on is: an official GLUE/SQUaD task: (give the name) my own task or dataset To reproduce ...
['It', "'", 's', 'imp', '##oli', '##te', 'to', 'love', 'again']# 2、 映射ids = tokenizer.convert_tokens_to_ids(token)# [1135, 112, 188, 24034, 11014, 1566, 1106, 1567, 1254]# 3、 将映射后的数字再重新转变为文本str = tokenizer.decode(ids)# "It's impolite to ...
important; border: 1px solid rgb(231, 234, 237); border-radius: 3px; padding: 8px 4px 6px; margin-bottom: 15px; margin-top: 15px; width: inherit;"># encode仅返回input_ids tokenizer.encode("i like you") Out : [101, 1045, 2066, 2017, 102] * 对于...
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') sentence = "the red cube is at your left" tokens = ["[CLS]"] + tokenizer.tokenize(sentence) + ["[SEP]"] input_ids = torch.tensor(tokenizer.convert_tokens_to_ids(tokens)) ...
PreTrainedTokenizer tokenizer = modelLoader.getTokenizer(); // 对输入文本进行tokenization List<String> inputs = tokenizer.tokenize(text); List<Integer> inputIds = tokenizer.convert_tokens_to_ids(inputs); // 准备输入张量 Integer[] inputArray = inputIds.stream().toArray(Integer[]::new); ...
继承自PreTrainedTokenizer类,并且实现了基类的方法。 基类的方法: (1)__call__函数: 1__call__(2text,text_pair,add_special_tokens,padding,truncation,3max_length,stride,is_split_into_words,pad_to_multiple_of,4return_tensors,return_token_type_ids,5return_attention_mask,6return_overflowing_tokens,...