added_tokens_encoder 返回从字符串到索引的排序映射。 added_tokens_decoder 返回添加的标记在词汇表中的字典,索引到 AddedToken。 get_added_vocab 返回添加的标记在词汇表中的字典,标记到索引。 __len__ 返回完整词汇表的大小(包括添加的标记)。 num_special_tokens_to_add 返回在编码序列时添加的特殊标记的数...
【说明】:我们前面自己训练的tokenizer,vocab_size只有10000,所以原本ChatGLM中的added_tokens_decoder,对于我们自己的tokenizer来说有些大了,如过需要的话,可以调整为从10001开始的数字。 6 tokenizer词表合并 之所以需要对tokenizer这么底层的模块动手,大多是为了,在垂直领域的数据上有一些专有词汇,并没有被通用的token...
然后定义special_tokens,指定词汇表大小为2048,其中三个特殊的token:<unk>(未知词),(开始标记),(结束标记)。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 texts=read_texts_from_jsonl(data_path)tokenizer.train_from_iterator(texts,trainer=trainer)tokenizer.decoder=decoders.ByteLevel()tokenizer_dir=...
BertTokenizerFast(name_or_path='uer/roberta-base-finetuned-dianping-chinese', vocab_size=21128, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD...
"added_tokens_decoder": { "0": { "content": "[UNK]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "1": { "content": "[PAD]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "spe...
additional_special_tokens=None, sep_token="[SEP]", cls_token="[CLS]", tokenize_chinese_chars=True, strip_accents=None, offset=100, pre_tokenizer=lambda x: jieba.cut(x, HMM=False), **kwargs): self.offset = offset if additional_special_tokens is not None: ...
self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token} self.offset = len(self._added_tokens_decoder) self._utf_vocab_size = 2**8 # utf is 8 bits # Load byte maps self.byte_maps = json.load(open(vocab_file, "r")) self.decompose_rewriter = ByteRewri...
"added_tokens_decoder": { "151643": { "content": "<|endoftext|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "151644": { "content": "<|im_start|>", "lstrip": false, "normalized": false, "rstrip":...
adding new tokens to the vocabulary, as it requires huge number of training iterations as well as the data to learn the new token embedding. However, if you want to add a new token if your application demands so, then it can be added as follows: num_added_toks = tokenizer.add_tokens...
"added_tokens_decoder": { "0": { "content": "<unk>", "lstrip": False, "normalized": False, "rstrip": False, "single_word": False, "special": True }, "1": { "content": "", "lstrip": False, "normalized": False, "rstrip...