将文本序列列表提供给tokenizer时,可以使用以下选项来完成所有这些操作(即设置padding=True, truncation=True,return_tensors="pt"): batch = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt") print(batch) # {'input_ids': tensor([[ 101, 8667, 146, 112, 182, 170, 142...
ids = tokenizer.encode(sen, add_special_tokens=True) ids 编码的结果 #将id序列转换为字符串,又称之为解码 str_sen = tokenizer.decode(ids, skip_special_tokens=False) str_sen 解码的结果 Step5 填充与截断 # 填充 ids = tokenizer.encode(sen, padding="max_length", max_length=15) ids 填充的结...
mGPT 模型和mT5 模型都使用的 MT5Tokenizer 分词器,我们看看两个模型文件中分词器的区别。 mGPT 模型文件: mT5 模型文件: 由于MT5Tokenizer 基于 SentencePiece 分词算法实现,所以两个模型的spiece.model文件相同,tokenizer_config.json和special_tokens_map.json大致相同。 总结: 在选择 tokenizer 时,需要根据具体的...
padding="max_length", truncation=True, max_length=6, add_special_tokens=True, return_tensors="tf", return_token_type_ids=False) 1. 2. 3. 4. 5. 6. 7. 对于上述代码, 如果自己提前处理好数据: A B C [PAD] [PAD] [PAD]则tokenizer返回的attention_mask为 1 1 1 1 1 1 如果数据是 A...
def text_enc(prompts, maxlen=None): ''' A function to take a texual promt and convert it into embeddings ''' if maxlen is None: maxlen = tokenizer.model_max_length inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt") ...
label = ro_tokenizer(sample['ro'], padding='max_length', max_length=120, truncation=True) input["decoder_input_ids"] = label["input_ids"] input["decoder_attention_mask"] = label["attention_mask"] input["labels"] = label["input_ids"] ...
def text_enc(prompts, maxlen=None): ''' A function to take a texual promt and convert it into embeddings ''' if maxlen is None: maxlen = tokenizer.model_max_length inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt") ...
tokenizer迁移huggingface的逻辑 1、迁移了huggingface的 PreTrainedTokenizer 逻辑,没有迁 PreTrainedTokenizerFast 的...
def text_enc(prompts, maxlen=None): ''' A function to take a texual promt and convert it into embeddings ''' if maxlen is None: maxlen = tokenizer.model_max_length inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt") ...
Tokenizer的作用是: 1、分词 2、将每个分出来的词转化为唯一的ID(int类型)。 pt_batch = tokenizer( ["We are very happy to show you the 🤗 Transformers library.","We hope you don't hate it."], padding=True, truncation=True, max_length=5, ...