So, let’s build the WordPiece tokenizer from scratch to understand everything that’s going under the hood.Our approach will be twofold: first, we’ll construct a mental framework by employing various illustrations to clarify the concepts. Then, we’ll put theory into practice by trainin...
def tokenize_and_align_labels(examples): tokenized_inputs = xlmr_tokenizer(examples["tokens"], truncation=True, is_split_into_words=True) labels = [] for idx, label in enumerate(examples["ner_tags"]): word_ids = tokenized_inputs.word_ids(batch_index=idx) previous_word_idx = None labe...
tokenize(text, never_split=self.all_special_tokens): # If the token is part of the never_split set if token in self.basic_tokenizer.never_split: split_tokens.append(token) else: split_tokens += self.wordpiece_tokenizer.tokenize(token) else: split_tokens = self.wordpiece_tokenizer.tokenize(...
split(' ')) if _new_token != token: word_corpus[_new_token] = word_corpus[token] word_corpus.pop(token) return word_corpus, bi_cnt 从上面代码可以看出,BPETokenizer和WordPieceTokenizer的差别只在每一步迭代的 进一步对比代码可以看出,BPETokenizer和WordPieceTokenizer只在"选出频次最大的二元组" ...
Let’s try another — this is a good one if you ever find yourself in Italy: And finally, let’s tokenize something that will be split into multiple word pieces: And that’s everything we need to build and apply our Italian Bert tokenizer!
append(1) return mask_token_ids, is_masked get_mask_token_ids函数的mask_token_id参数指的是[mask]这个token的id。mask_rate参数是mask操作的概率,在BERT里这个概率通常是15%。vocab_size参数指的是tokenizer的词典的大小,有了它才能将被mask掉的token替换成随机的token。
tokens = tokenizer.tokenize(line) if tokens: all_documents[-1].append(tokens) #二维列表 [文章,句子] # Remove empty documents all_documents = [x for x in all_documents if x] #删除空列表 rng.shuffle(all_documents) #随机排序 vocab_words = list(tokenizer.vocab.keys()) ...
The training is identical -- we still predict each masked WordPiece token independently. The improvement comes from the fact that the original prediction task was too 'easy' for words that had been split into multiple WordPieces.This can be enabled during data generation by passing the flag --...
tokenizer:BertTokenizer, max_len: int, pairs: Iterable[Tuple[str, int]] ):pairs = [(text.split()[:max_len], label)fortext, labelinpairs] texts, labels = zip(*pairs) labels = torch.LongTensor(labels)# +1 for [CLS] tokentext_lens = torch.LongTensor([len(text)+1fortextintexts])...
1 BertTokenizer(Tokenization分词) 组成结构:BasicTokenizer和WordPieceTokenizer BasicTokenizer主要作用: 按标点、空格分割句子,对于中文字符,通过预处理(加空格方式)进行按字分割 通过never_split指定对某些词不进行分割 处理是否统一小写 清理非法字符 WordPieceTokenizer主要作用: ...