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量。 tokenize 将字符串转换为标记序列,使用 Tokenizer。 _tokenize* 将字符串转换为标记序列,使用 Tokenizer。不考虑addedtoken。 con...
vocab:一个字典 Dict[str, int],指定字符串 key 及其id ,表示词表。 unk_token:一个字符串,指定 unknown token。 max_input_chars_per_word:一个整数,指定一个单词中允许的最大字符数。 方法: from_file(vocab, **kwargs) -> WordPiece:从文件中初始化一个 WordPiece。 参数:vocab:vocab.json 文件的路...
eos_token_id vocab_size = tokenizer.vocab_size for v in tokenizer.get_added_vocab().values(): if v >= vocab_size: byte_tokens.append(bytes(tokenizer.convert_tokens_to_string(['a', tokenizer.convert_ids_to_tokens(v)])[1:], encoding="utf8")) After reinstalling the package with this...
Hi, I want to train a tokenizer with code like the following # I am not sure about the correct way, so I try to add '<sep>' in every possible way. trainer = BpeTrainer(special_tokens=["<unk>", "<pad>", '<sep>'], vocab_size=vocab_size, ) ...
vocab_files = {},init_configuration = {} 接下来调用if else的部分 else:# At this point pretrained_model_name_or_path is either a directory or a model identifier nameadditional_files_names={"added_tokens_file":ADDED_TOKENS_FILE,"special_tokens_map_file":SPECIAL_TOKENS_MAP_FILE,"tokenizer_co...
vocab_size: int =5000, min_frequency: int =2, save_path: str ="", added_tokens: List[str] = [], bos_token: str ="<|endoftext|>", eos_token: str ="<|endoftext|>", unk_token: str ="<|endoftext|>", )->None:""" ...
self.sp_model.Load(str(vocab_file)) 需要注意的是,XLMRobertaModel是fairseq下的模型,那么其特殊字符的加入位置是不一样的,另外XLMRobertaModel在末尾加了<mask>字符 计算流程 一个query字符串近来的流程是怎样的呢,首先经过query会经过分词变成多个token piece,具体分词算法是bpe,然后模型字典中找token piece对应的...
def get_vocab(self): return dict(self.vocab, **self.added_tokens_encoder) def _tokenize(self, text): split_tokens = [] # print("pegasus_tokenizer: ", text) for text in self.pre_tokenizer(text): if text in self.vocab: ...
def create_tokenizer_from_hub_module(): """Get the vocab file and casing info from the Hub module.""" with tf.Graph().as_default(): bert_module = hub.Module(FLAGS.bert_hub_module_handle) tokenization_info = bert_module(signature="tokenization_info", as_dict=True) with tf.Session()...
First, in the `initialize_vocab` method, we initialize the vocabulary by getting all the words and their counts, then initialize the tokens by finding all irreducible characters. The get_bigrams method is auxiliary method to determine the most frequent bigram. The merge vocab takes care of updat...