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,...
wrong_targets=tokenizer(fr_sentence)print(tokenizer.convert_ids_to_tokens(wrong_targets["input_ids"]))print(tokenizer.convert_ids_to_tokens(targets["input_ids"]))['▁Par','▁dé','f','aut',',','▁dé','ve','lop','per','▁les','▁fil','s','▁de','▁discussion','']['▁Pa...
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))# 注意这里会把subword自动拼起...
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...
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(text1,text2,..) 的结果: input_ids:list,(每个token 对应的在词典中对应的id) 总是作为唯一必须的输入参数传入到模型中 attention_mask:list,是一个可选的参数,used when batching sequences together,这个参数告诉模型哪些token应该被attend,哪些不应该。见下方使用案例。 token_type_ids:list,目的是...
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...
['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 ...
tokenizer.decode(encoded_input["input_ids"]) '[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]' 1. 2. 正如您所看到的,tokenizer 向句子添加了两个特殊的令牌 – CLS 和 SEP (分类器和分隔符)。并非所有模型都需要特殊标记,但如果需要,tokenizer 将...
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); ...