我们本文使用的模型是uer/roberta-base-finetuned-dianping-chinese 这个模型可以里面的tokenizer的实现分为了rust实现和python实现,rust实现比较快我们代码中的fast_tokenizer 就是用rust来实现的,并且生成的对象的快慢我们通过use_fast=False这个参数来判断的 当我们使用 fast_tokenizer 时并且把参数return_offsets_mappin...
102: AddedToken("[SEP]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), 103: AddedToken("[MASK]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), } 1. 2. 3. 4. 5. 6. 7. 构造Tokenizer 时,可以通过传入use_fast=False...
After #114 , the server decodes the running sequences every step. This leads to significant overhead, especially when the slow tokenizer is used (e.g., LLaMA). # opt-13b inference latency (bs 8, input 32, output 128) Avg latency: 3.57 se...
Fast Tokenizer示例 fast_tokenizer = AutoTokenizer.from_pretrained("uer/roberta-base-finetuned-dianping-chinese") print(fast_tokenizer) Slow Tokenizer示例 slow_tokenizer = AutoTokenizer.from_pretrained("uer/roberta-base-finetuned-dianping-chinese", use_fast=False) print(slow_tokenizer) 性能对比 # Fa...
slow_tokenizer = AutoTokenizer.from_pretrained("uer/roberta-base-finetuned-dianping-chinese", use_fast=False) slow_tokenizer ''' BertTokenizer(name_or_path='uer/roberta-base-finetuned-dianping-chinese', vocab_size=21128, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side...
Closed 当前行为 | Current Behavior 准备将本地词表合并到Qwen的词表,但是发现Qwen tokenizer无论是fast还是普通的use_fast=False,也就是tokenization_qwen2.py和tokenization_qwen2_fast.py,均不支持“sp_model”,导入报错: 1.AttributeError: 'Qwen2Tokenizer' object has no attribute 'sp_model' ...
use_fast_tokenizer 原理use_fast_tokenizer原理 use_fast_tokenizer是一个布尔值参数,用于指定是否使用快速的tokenizer。在某些情况下,使用快速的tokenizer可以加快模型训练和推理速度。 其原理主要在于快速tokenizer的设计和实现方式。一般来说,传统的tokenizer需要进行复杂的词法分析,逐字符或逐词进行解析和编码,这个过程...
apply_residual_connection_post_layernorm ... False async_tensor_model_parallel_allreduce ... True attention_dropout ... 0.0 attention_softmax_in_fp32 ... True barrier_with_L1_time ... True bert_binary_head ... True bert_embedder_type ... megatron...
class tokenizers.pre_tokenizers.ByteLevel(add_prefix_space = True, use_regex = True):ByteLevel PreTokenizer ,将给定字符串的所有字节替换为相应的表示并拆分为单词。 参数: add_prefix_space:是否在第一个单词前面添加空格,如果第一个单词前面目前还没有空格。 use_regex:如果为 False 则阻止该 pre_token...
DefaultV1Recipe.ComponentType.MESSAGE_TOKENIZER, is_trainable=False )classBertTokenizer(Tokenizer):def__init__(self,config:Dict[Text, Any] = None)->None:""" :param config: {"pretrained_model_name_or_path":"", "cache_dir":"", "use_fast":""} ...