LLaVA-NeXT/llava/train/train.py Lines 1261 to 1263 in 79ef45a if self.tokenizer.pad_token_id is None: # self.tokenizer.pad_token_id = self.tokenizer.eos_token_id # FIXME: this could only be triggered for llama3 model. self.tokenizer.pad_...
eos_token_id # a transformer tokenizer was given with byte_decoder elif hasattr(tokenizer, "convert_ids_to_tokens"): byte_tokens = [bytes(tokenizer.convert_tokens_to_string(['a', tokenizer.convert_ids_to_tokens(i)])[1:], encoding="utf8") for i in range(tokenizer.vocab_size)] bos_...
当新的输入序列被提供给模型时,单词会被转换为带有相关token ID的tokens,该ID对应于该token在tokenizer词汇表中的位置。例如,单词cat可能位于tokenizer词汇表的第349个位置,因此其ID为349。Token IDs用于创建one-hot编码的向量,以从权重矩阵中提取正确的learned embeddings(即,一个V维向量,其中每个元素都是0,除了在to...
The CLS token and Bi-LSTM outputs are fed into two fully connected neural networks (FCNNs). This process is discussed in Sect. 3.2. The testing was carried out using the SOLID testing set, one of the competition datasets, which adheres to the rules of the SemEval-2020 Task 12 (OffensE...
我们将使用 keras_nlp.tokenizers.WordPieceTokenizer 层进行标记化 文本。keras_nlp.tokenizers.WordPieceTokenizer 采用WordPiece 词汇表 并具有用于标记文本和取消标记序列的功能。 在定义分词器之前,我们首先需要在数据集上训练它 我们有。WordPiece 标记化算法是一种子词标记化算法; 在语料库上训练它为我们提供了一个...
It is noted that our alignment method does not include the alignment of [cls] token according to Section 3.2, which unleashes the poten- tial of the method for training in arbitrary scenarios. \mathcal {L}_{s}(i) = ||pred_{ori_i} - sg[pred_{aug_i}]|| + ||...
('-inf')).masked_fill(mask==1,float(0.0))returnmask# def generate_key_padding_mask(self, src, pad_id=0):# f = torch.full_like(src,False).bool().to()# t = torch.full_like(src,True).bool()# return torch.where(src==pad_id,t,f)defforward(self,x,key_mask=None,sq_mask=...
= self.tokenizer.pad_token_id).sum().item() - if token_count > self.max_length: - print("The text has been truncated.") - - return { - 'input_ids': inputs['input_ids'].squeeze(0), - 'attention_mask': inputs['attention_mask'].squeeze(0), - 'labels': torch.tensor(label,...
{}, "remove_invalid_values": false, "repetition_penalty": 1.0, "return_dict": true, "return_dict_in_generate": false, "sep_token_id": null, "task_specific_params": null, "temperature": 1.0, "tie_encoder_decoder": false, "tie_word_embeddings": true, "tokenizer_class": null, "...
(100))## 7765*2fromtokenizerimportTokenizertokenizer=Tokenizer(config.vocab_size,config.max_seq_len)tokenizer.build_vocab(df.review)## 从所有的训练数据中建立数据对应的词汇表,建立id2word 和 word2id 的字典token_res=tokenizer(["你好","你好呀"])## 分词,词转ID,然后用Id值构成新序列,注意找到一...