trainer = WordPieceTrainer( vocab_size=30522, special_tokens=["[UNK]","[CLS]","[SEP]","[PAD]","[MASK]"] ) files = [ f"data/wikitext-103-raw/wiki.{split}.raw" forsplitin["test","train","valid"] ] bert_tokenizer.train(files, trainer) bert_tokenizer.sa...
train(files=["data/corpus.txt"], trainer=trainer) 其中BpeTrainer的参数说明如下: vocab_size: 表示最终词汇表的大小,包括所有的词元和字母。 show_progress: 一个布尔值,用于决定在训练过程中是否显示进度条。 special_tokens: 一个特殊词元的列表,这些词元需要模型识别。 initial_alphabet: 初始字母表,即使在...
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel() trainer = trainers.BpeTrainer(special_tokens=["<pad>","","","<unk>","<mask>"]) tokenizer.train(files=["my_dataset.txt"], trainer=trainer) tokens = tokenizer.encode("unaffable").tokens print(tokens) # 输出: ['un','aff','able'...
train(files=["wiki.train.raw", "wiki.valid.raw", "wiki.test.raw"], trainer=trainer) #使用训练完的tokenizer来编码文本 output = tokenizer.encode("Hello, y'all! How are you ?") print(output.tokens) # ["Hello", ",", "y", "'", "all", "!", "How", "are", "you", "[UNK]...
TrainFromFiles(Trainer trainer, ReportProgress reportProgress, params string[] files): 使用输入文件训练标记器模型。 主要属性: Model: 获取或设置标记器使用的模型。 PreTokenizer: 获取或设置标记器使用的预处理器。 Normalizer: 获取或设置标记器使用的规范化器。
tokenizer.train(data_files, trainer) #方式二 # tokenizer.train_from_iterator(batch_iterator(), trainer=trainer, length=len(dataset['train'])) # tokenizer保存 tokenizer.save( "data/tokenizer-wiki.json" ) # tokenizer加载 tokenizer = tokenizer.from_file( "data/tokenizer-wiki.json" ) sentence =...
“Normalizer, Trainer, Encoder, Decoder” 其中Normalizer用来对Unicode编码进行规范化,这里使用的算法是NFKC方法,同时也支持自定义规范化方法。Trainer则用来训练分词模型。Encoder是将句子变成编码,而Decoder是反向操作。他们之间存在以下函数关系: $$ Decode(Encode(Normalize(text))) = Normalize(text): ...
(" ", "<space>") ]) tokenizer.pre_tokenizer = PretokenizerSequence([ Split("\n", behavior="removed") ]) trainer = BpeTrainer( special_tokens=special_tokens, vocab_size=10000, min_frequency=2, ) tokenizer.train(files=[corpus_file], trainer=trainer) tokenizer.save("example_tokenizer.json...
Then training your tokenizer on a set of files just takes two lines of codes: fromtokenizers.trainersimportBpeTrainertrainer=BpeTrainer(special_tokens=["[UNK]","[CLS]","[SEP]","[PAD]","[MASK]"])tokenizer.train(files=["wiki.train.raw","wiki.valid.raw","wiki.test.raw"],trainer=train...
()) tokenizer.pre_tokenizer = Whitespace() # 训练分词器(这里以训练一个简单的BPE分词器为例) files = ["path/to/your/training/file.txt"] trainer = BpeTrainer(special_tokens=["[PAD]", "[UNK]", "[CLS]", "[SEP]"]) tokenizer.train(files, trainer) # 使用分词器对文本进行编码 text = ...