features实例化需要使用字典类型,并且和_generate_examples函数中的返回数据中的字典完全一致,见下图右。数据标签的feature是datasets. features.ClassLabel,实例化参数可以使用列表数据类型,其中包含每个标签的名称(如cifar的"airplane","automobile"...),也可以使用int数据类型,直接写入类别数。task_templates传入常见的任务...
每个arrow_dataset都有多少条数据,以及这些数据的feature是什么。 整个数据结构是三层的,我们把每层的type打印出来看一下就知道,第一层也就是所有的数据内容:DatasetDict'>,这里是构造的一个词典类,顾名思义他是把所谓的dataset打包成一个字典结构,第二层也就是数据层,dataset类处理的数据,这里每个都是一个dataset...
{ "text": DatasetInfo.Feature(dtype="string"), "label": DatasetInfo.Feature(dtype="int32"), }), supervised_keys=None, homepage="http://example.com", citation="", ) def _split_generators(self, dl_manager): # 这里假设你的数据已经打包成一个压缩文件,或者你可以直接指定文件路径 data_dir...
train_features, train_labels = next(iter(train_dataloader)) print(f"Feature batch shape: {train_features.size()}") print(f"Labels batch shape: {train_labels.size()}") img = train_features[0].squeeze() label = train_labels[0] plt.imshow(img, cmap="gray") plt.show() print(f"Label...
Hi @Alberto1404, to load an object detection dataset it's recommended to make use of the metadata feature as explained here. Alberto1404 commented Apr 3, 2023 Thank you @NielsRogge! It works!!! 🎉 1 Contributor mariosasko commented Jul 24, 2023 You can now refer to https://hugging...
(feature=Value(dtype='string', id=None), length=-1, id=None), 'ability_zh': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'cate_ability_en': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'cate_ability_zh': Sequence(feature=...
Use map() for transformations like tokenization or feature engineering. Leverage built-in methods like shuffle(), filter(), and train_test_split() to prepare the data dynamically without external libraries. In conclusion The load_dataset function is best for getting started quickly with pre-built...
feature_set}_{args.fea_dim}.pkl' data_file = os.path.join(f'./data_cache/{args.task}/', data_file_name) if args.use_personality ==True and args.use_emotion==False: data_file = os.path.join(f'./data_cache/personality_{args.task}/', data_file_name) elif args.use_...
feature batch shape:torch.Size([64, 1, 28, 28]) label batch shape:torch.Size([64]) 1. 2. label:4 1. (二)Transforms 数据并不总是以训练机器学习算法所需的最终处理形式出现。我们使用transforms对数据进行一些操作,使其适合训练。所有的TorchVision数据集都有两个参数transform(修正特征),target_transf...
{if (feature.Fields.get_Field(i).Type == esriFieldType.esriFieldTypeRaster) { iRasterField = i; i = 1000; } }//Create raster value with input raster dataset. IRasterValue rasterValue =new RasterValueClass(); rasterValue.RasterDataset = rasterDataset;//Set raster value to the raster fi...