tokenizer = old_tokenizer.train_new_from_iterator(datasets_sample, 52000) tokens = tokenizer.tokenize(example) # 打印出来看看,略有不同 print(tokens) #新训练的分词器可以保存起来,注意这里用的是AutoTokenizer tokenizer.save_pretrained( "code-search-net-tokenizer" ) Tokenizer的其他功能 第一个是编码相...
wrong_targets=tokenizer(fr_sentence)print(tokenizer.convert_ids_to_tokens(wrong_targets["input_ids"]))print(tokenizer.convert_ids_to_tokens(targets["input_ids"]))['▁Par','▁dé','f','aut',',','▁dé','ve','lop','per','▁les','▁fil','s','▁de','▁discussion','']['▁Pa...
而通过convert_tokens_to_ids得到的ids是: 代码语言:javascript 复制 [2052,1110,170,1363,1285,1106,3858,11303,1468] 可以发现,前者在头和尾多了俩token,id分别是 101 和 102。 decode出来瞅瞅: 代码语言:javascript 复制 tokenizer.decode([101,2052,1110,170,1363,1285,1106,3858,11303,1468,102]) 输出: ...
b、其他tokenizer基于当前仓上逻辑进行适配,原因为 i、clip的huggingface代码没跑通 ii、bloom的huggingface逻辑未继承PreTrainedTokenizer,另修复bloom的tokenizer的token_type_id长度异常问题 iii、glm未在transformers的github仓库开源。另,glm的tokenizer去除padding_side的入参,配置文件(包括obs中的)也删除该配置项 iiii、...
直接传入单个文本,tokenizer的返回值是一个字典,value都是list。inputs_id键的值是token的数值表示(每个值对应一个token),attention_mask表示哪些token是需要进入模型(每个值对应一个token,数值为1表示对应的token需要进入模型)。 在后文glossary部分(我撰写的笔记博文:huggingface.transformers术语表)对这些键的意义有更...
from transformers import AutoTokenizer, AutoModelForCausalLM device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "gpt2-xl" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name).to(device) ...
基本用法 这是huggingface设计的一种新格式，大致就是以更加紧凑、跨框架的方式存储Dict[str, Tensor]，...
In the newer versions of Transformers (it seems like since 2.8), calling the tokenizer returns an object of class BatchEncoding when methods __call__, encode_plus and batch_encode_plus are used. You can use method token_to_chars that takes the indices in the batch and returns the characte...
# implementation and a “Fast” implementation based on the Rust library Tokenizers. # The “Fast” implementations allows a significant speed-up in particular # when doing batched tokenization, and additional methods to map between the # original string (character and words) and the token space...
tokenized_sentences_1=tokenizer(raw_train_dataset['sentence1'])tokenized_sentences_2=tokenizer(raw_train_dataset['sentence2']) 但对于MRPC任务,我们不能把两个句子分开输入到模型中,二者应该组成一个pair输进去。 tokenizer也可以直接处理sequence pair: ...