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...
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,), ("...
CONFIG_MAPPING=OrderedDict([("retribert",RetriBertConfig,),("t5",T5Config,),("mobilebert",MobileBertConfig,),("distilbert",DistilBertConfig,),("albert",AlbertConfig,),("camembert",CamembertConfig,),("xlm-roberta",XLMRobertaConfig,),("marian",MarianConfig,),("mbart",MBartConfig,),("bart...
+ ([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:...
接下来,让我们实例化一个模型。该模型将从头开始训练,因此我们不使用 from_pretrained 方法,而是从 config 中随机初始化模型。 我们为模型指定了几个附加参数: prediction_length (在我们的例子中是 24 个月) : 这是 Transformer 的解码器将学习预测的范围; ...
[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]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", ...
return_offsets_mapping:在做序列标注、信息抽取等任务时,我们获取的原始数据标签是严格对应于原始的文本字符,于是在tokenizer处理后位置会变得不一样,因此需要返回offset_mapping,知道被处理后的每个token是对应于原始的哪些字符; inputs = fast_tokenizer(sen, return_offsets_mapping=True) ...