1.1 train tokenizer vs train model 2 基于旧的tokenizer重训一个新的tokenizer 2.1 第一步:收集训练数据 2.2 第二步:将dataset 变成 iterator of lists of texts 2.2.1 最佳实践代码:使用generator或者yield 2.3 第三步:训练新的tokenizer 2.4 第四步:使用 tokenizer 2.5 第四步:保存 tokenizer 2.6 第五步:共...
要加载分词器,你需要创建一个分词器对象。要执行此操作,需再次将model_id作为参数传递给AutoTokenizer类的.from_pretrained方法。 请注意,本例中还使用了其他一些参数,但当前而言,理解它们并不重要,因此我们不会解释它们。 tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True, padding_side='l...
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、pangualpha的tokenizer,huggingface不包含 5、本次测试范围为 a、tokenizer:["gpt2", "bert_base_uncased", "llama_7b", "bloom_560m", "pangualpha_2_6b", "clip_vit_b_32", "glm_6b", "t5_small"] b、接口:...
要执行此操作,需再次将model_id作为参数传递给AutoTokenizer类的.from_pretrained方法。 请注意,本例中还使用了其他一些参数,但当前而言,理解它们并不重要,因此我们不会解释它们。 tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True, padding_side='left') 分词器是什么? 分词器负责将句子分...
[EOS]', text_pair=None, # 当句子长度大于max_length时,截断 truncation=True, # 一律补pad到max_length长度 padding='max_length', add_special_tokens=True, max_length=8, return_tensors=None, ) print(out) # [101, 21128, 4638, 3173, 21129, 21130, 102, 0] tokenizer.decode(out) # '[...
tokenizer_not_use_fast ... True tokenizer_padding_side ... right tokenizer_type ... Llama2Tokenizer tp_comm_bulk_dgrad ... True tp_comm_bulk_wgrad ... True tp_comm_overlap ... False tp_comm_overlap_cfg ...
if maxlen is None: maxlen = tokenizer.model_max_length inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt") return text_encoder(inp.input_ids.to("cuda"))[0].half() vae, unet, tokenizer, text_encoder, scheduler = load_artifacts() ...
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", truncation_side="left") print(tokenizer.truncation_side) right Expected behavior left Possible solution I believe the problem is in the missing part attokenization_utils_base.py(just like the one for the padding side athttps://github.com/huggin...
if maxlen is None: maxlen = tokenizer.model_max_length inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt") return text_encoder(inp.input_ids.to("cuda"))[0].half() vae, unet, tokenizer, text_encoder, scheduler = load_artifacts() ...
if maxlen is None: maxlen = tokenizer.model_max_length inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt") return text_encoder(inp.input_ids.to("cuda"))[0].half() vae, unet, tokenizer, text_encoder, scheduler = load_artifacts() ...