首先将num_merges赋值给self.max_merge_times,然后遍历num_merges次,每次迭代中,先调用get_stats方法获取词汇表中所有相邻字符对的频率,如果pairs为空,则说明没有能合并的字符对了,合并完成跳出循环,否则调用merge_vocab方法把最高频的字符对合并为一个新的字符,并将合并的字符对和当前迭代次数写入到self.merge_rules...
vocab[id_] def get_vocab(self): return self.token2id tokenizer = myTokenizer("vocab.txt") tokenizer(["1!123"]) # {'input_ids': [[1, 6, 1, 2, 3]], 'token_type_ids': [[0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1]]} # vocat.txt 0 ...
vocab:vocab.json 文件的路径。 un_token:一个字符串,指定 unknown token。 read_file(vocab) -> Dict[str, int] :从文件中读取词表。 参数:参考 from_file。 class tokenizers.models.WordPiece( vocab, unk_token, max_input_chars_per_word):WordPiece 模型。 参数: vocab:一个字典 Dict[str, int],...
tokenizer.vocabulary = tokenizer.get_vocab() ^^^ AttributeError: 'MistralTokenizer' object has no attribute 'get_vocab' Update : With the argumentguided-decoding-backend = lm-format-enforcer, I get a TypeError Traceback (most recent call last): File "path/to/venv/venv_happyvllm/lib/python3...
Bpe.GetVocab 方法 參考 意見反應 定義 命名空間: Microsoft.ML.Tokenizers 組件: Microsoft.ML.Tokenizers.dll 套件: Microsoft.ML.Tokenizers v0.21.1 取得字典將權杖對應至識別碼。 C# 複製 public override System.Collections.Generic.IReadOnlyDictionary<string,int> GetVocab (); 傳回 IReadOnly...
(self,vocab,tokens,num_merges):merges=[]foriinrange(num_merges):pairs=self.get_bigram_counts(vocab)best_pair=max(pairs,key=pairs.get)best_count=pairs[best_pair]vocab,(bigram,bytepair)=self.merge_vocab(best_pair,vocab)merges.append((r'(?<!\S)'+bigram+r'(?!\S)',bytepair))tokens[...
public override System.Collections.Generic.IReadOnlyDictionary<string,int> GetVocab (); 返回 IReadOnlyDictionary<String,Int32> 适用于 产品版本 ML.NET Preview 反馈 即将发布:在整个 2024 年,我们将逐步淘汰作为内容反馈机制的“GitHub 问题”,并将其取代为新的反馈系统。 有关详细信息,请参阅:https://...
vocab = tokenizer.get_vocab() print(len(vocab)) print(tokenizer.vocab_size) # tokenizer.save_vocabulary(save_directory="test") # with open('vocab_utf8.txt', 'w', encoding='utf-8') as f: # json.dump(vocab, f, indent=4) text = "家牛的体重范围是多少?" + "\n" # encode_1 =...
vocab = {} for word in all_words: word = self.format_word(word) vocab[word] = vocab.get(word, 0) + 1 tokens = collections.Counter(text) return vocab, tokens def get_bigram_counts(self, vocab): pairs = {} for word, count in vocab.items(): ...
get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} class PegasusTokenizer(PreTrainedTokenizer): r""" Construct a Pegasus tokenizer. Based on WordPiece.This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to ...