raw_datasets=load_dataset(extension,data_files=data_files,cache_dir=model_args.cache_dir,use_auth_token=Trueifmodel_args.use_auth_tokenelseNone,) 如果没有设置 validation_file,则取 5% 的数据作为验证集 加载模型配置文件,优先从 config_name -> model_name_or_path -> CONFIG_MAPPING[model_args.mo...
在代码库中通常命名为,「XXXForSequenceClassification」or「XXXForMaskedLM」,其中XXX是模型的名称(如Bert), 结尾是预训练任务的名称 (MaskedLM) 或下游任务的类型(SequenceClassification)。 另外,针对上述三大类,transformer还额外封装了「AutoConfig, AutoTokenizer,AutoModel」,可通过模型的命名来定位其所属的具体类,...
复制 CONFIG_MAPPING=OrderedDict([("retribert",RetriBertConfig,),("t5",T5Config,),("mobilebert",MobileBertConfig,),("distilbert",DistilBertConfig,),("albert",AlbertConfig,),("camembert",CamembertConfig,),("xlm-roberta",XLMRobertaConfig,),("marian",MarianConfig,),("mbart",MBartConfig,),("...
先上一张框架图 # 导包importloggingimportmathimportosimportsysimportwarningsfromdataclassesimportdataclass,fieldfromitertoolsimportchainfromtypingimportOptionalimportdatasetsimportevaluateimporttorchfromdatasetsimportload_datasetimporttransformersfromtransformersimport(CONFIG_MAPPING,MODEL_FOR_CAUSAL_LM_MAPPING,AutoConfig,A...
CONFIG_MAPPING = OrderedDict( [ ("retribert", RetriBertConfig,), ("t5", T5Config,), ("mobilebert", MobileBertConfig,), ("distilbert", DistilBertConfig,), ("albert", AlbertConfig,), ("camembert", CamembertConfig,), ("xlm-roberta", XLMRobertaConfig,), ("marian", MarianConfig,), ("...
CONFIG_MAPPING = OrderedDict( [ ("retribert", RetriBertConfig,), ("t5", T5Config,), ("mobilebert", MobileBertConfig,), ("distilbert", DistilBertConfig,), ("albert", AlbertConfig,), ("camembert", CamembertConfig,), ("xlm-roberta", XLMRobertaConfig,), ("marian", MarianConfig,), ("...
[str, TensorType]] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool ...
+ ([FieldName.FEAT_DYNAMIC_REAL] if config.num_dynamic_real_features > 0 else []), ), # step 8: rename to match HuggingFace names RenameFields( mapping={ FieldName.FEAT_STATIC_CAT: "static_categorical_features", FieldName.FEAT_STATIC_REAL: "static_real_features", FieldName.FEAT_TIME:...
def__init__(self, config: IdeficsConfig, embed_dim:int, depth:int, n_heads:int, head_dim:int, n_latents:int):""" 初始化函数,创建一个 IdeficsPerceiverResampler 对象。 参数: - config: IdeficsConfig 对象,包含了模型的配置信息 - embed_dim: 整数,嵌入维度,用于定义输入的特征维度 ...
+ ([FieldName.FEAT_DYNAMIC_REAL]ifconfig.num_dynamic_real_features >0else[]), ), # 步骤 8: 建立字段名和 Hugging Face 惯用字段名之间的映射 RenameFields( mapping={ FieldName.FEAT_STATIC_CAT:"static_categorical_features", FieldName.FEAT_STATIC_REAL:"static_real_features", ...