getattr(module, "MarianTokenizer") 传递给了 tokenizer_class, 最后 from_pretrained 这里可以开始看 tokenization_marian.py 的代码 :transformers/models/marian/tokenization_marian.py MarianTokenizer -> PreTrainedTokenizer -> PreTrainedTokenizerBase (继承关系) PreTrainedTokenizerBase: transformers/tokenization_utils...
# 需要导入模块: from transformers import AutoTokenizer [as 别名]# 或者: from transformers.AutoTokenizer importfrom_pretrained[as 别名]defget_defaults(self, model, tokenizer, framework):task_defaults = SUPPORTED_TASKS[self.task]ifmodelisNone:ifframework =="tf": model = task_defaults["tf"].from...
from_pretrained( args.model_name_or_path, do_lower_case=True ) seg_file( os.path.join(args.output_dir, args.data_split + ".txt.tmp"), tokenizer, args.max_len, ) seg_file( os.path.join(args.output_dir, args.data_split + "_box.txt.tmp"), tokenizer, args.max_len, ) seg_...
from transformers import AutoTokenizer model_id = "mistralai/Mistral-7B-Instruct-v0.3" auto_tokenizer = AutoTokenizer.from_pretrained(model_id) When I inspect auto_tokenizer variable, then I get LlamaTokenizerFast: LlamaTokenizerFast(name_or_path='mistralai/Mistral-7B-Instruct-v0.3', vocab_size...
fromtransformersimportAutoTokenizertokenizer=AutoTokenizer.from_pretrained("bert-base-uncased")text=["I love machine learning.","Hello, world!","Transformers are powerful models."]encoded_input=tokenizer.batch_encode_plus(text,add_special_tokens=True,max_length=10,# 指定最大序列长度padding="max_leng...
model = AutoModelForCausalLM.from_pretrained("gpt2")# 编码输入文本,增加返回的张量 input_text ="The meaning of life is" input_ids = tokenizer.encode(input_text, return_tensors='pt')# 生成文本 output = model.generate(input_ids, max_length=50)# 解码生成的文本 ...
model_name="dbmdz/bert-base-italian-xxl-cased"tokenizer=AutoTokenizer.from_pretrained(model_name)bert=TFBertModel.from_pretrained(model_name) 该模型将提供一系列意大利推文,并需要确定它们是否具有讽刺意味。 我在构建模型的初始部分时遇到了问题,该部分接受输入并将它们输入到令牌程序,以便获得我可以提供给BERT...
tokenizer = AutoTokenizer.from_pretrained( model_name, model_max_length = model_max_length, padding_side = padding_side, token = token, use_fast = False, ) return check_tokenizer( model = model, tokenizer = tokenizer, model_name = model_name, ...
from modelscope import AutoModelForCausalLM, AutoTokenizer # 指定模型名称 model_name = "qwen/Qwen2-0.5B-Instruct" # 加载模型和分词器 model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # 定义输入文本 prompt = "Give me a short introdu...
近日，PyTorch 社区又添入了「新」工具，包括了更新后的 PyTorch 1.2，torchvision 0.4，torchaudio 0.3 和 torchtext 0.4。每项工具都进行了新的优化与改进，兼容性更强，使用起来也更加便捷。PyTorch 发布了相关文章介绍了每个工具的更新细节，AI 开发者将其整理与编译如下。最近...