GitHub - openai/tiktoken: tiktoken is a fast BPE tokeniser for use with OpenAI's models. import tiktoken enc = tiktoken.get_encoding('gpt2') # cl100k_base print(enc.encode(' hello world!') '<|endoftext|>' 这个特殊 token,表明是另外一篇文章了。训练时需要清除模型中的所有记忆。 #! w...
(实际就是初始化和加载path中自定义的Tokennizer类,如ChatGLMTokenizer) *AutoTokenizer对于官方定义的预训练模型,如gpt2、bert等,不用在path中去读取定义token配置文件和Tokennizer类,会直接加载transformer内部已经定义的类和配置文件,如于GPT2Tokenizer;但是path中需要定义词表,会初始化给GPT2Tokenizer。(即AutoTokeniz...
We can verify that the RegexTokenizer has feature parity with the GPT-4 tokenizer from tiktoken as follows: text = "hello123!!!? (안녕하세요!) 😉" # tiktoken import tiktoken enc = tiktoken.get_encoding("cl100k_base") print(enc.encode(text)) # [15339, 4513, 12340, 30,...
importtorchfromcosmos_tokenizer.video_libimportCausalVideoTokenizermodel_name="Cosmos-0.1-Tokenizer-CV4x8x8"input_tensor=torch.randn(1,3,9,512,512).to('cuda').to(torch.bfloat16)# [B, C, T, H, W]encoder=CausalVideoTokenizer(checkpoint_enc=f'pretrained_ckpts/{model_name}/encoder.jit') (...
''' enc = tokenizer.encode_plus( text_to_encode, max_length=128, add_special_tokens=True, return_token_type_ids=False, return_attention_mask=False, )['input_ids'] print(tokenizer.convert_ids_to_tokens(enc)) Result: The input contains a series of questions and answers. The user ...
start_token=enc.encoder['<|endoftext|>']ifargs.unconditionalelseNone, batch_size=args.batch_size, temperature=args.temperature, top_k=args.top_k, device=device ) out = out[:, len(context_tokens):].tolist()foriinrange(args.batch_size): ...
基于char: 一个 char 对应于一个token,比如 "translation" 对应于 11 个 token; 基于word:一个 word 对应于一个 token,比如 “translation” 对应于 1 个 token; 基于sub-word:一个 sub-word(子词)对应于一个 token。子词是介于 char 和 word 之间的字符片段,比如 translation 可以拆分为 trans、la 和 ...
import tiktoken # GPT-2 (不合并空格) enc = tiktoken.get_encoding("gpt2") print(enc.encode(" hello world!!!")) # GPT-4 (合并空格) enc = tiktoken.get_encoding("cl100k_base") print(enc.encode(" hello world!!!")) ##[220, 220, 220, 23748, 995, 10185] ...
print(f"token: {token_filename}") print(f"encoder: {enc_model}") print(f"decoder: {dec_model}") print(f"language: {language}")# Split sentence sens = split_sentences_zh(sentence) _symbol_to_id = {s: i for i, s in enumerate(LANG_TO_SYMBOL_MAP[language])}#...
#gpt2_tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=True)gpt2_tokenizer=GPT2Tokenizer.from_pretrained("openai-community/gpt2")oai_tokenizer=tiktoken.get_encoding("gpt2")orig="Is this restaurant family-friendly ? Yes No Unsure ? This is an other sentence ."hf_enc=gpt2_tokeniz...