调用load_dataset函数,并传入本地数据集的路径作为参数: 使用load_dataset函数,并指定data_files参数为本地数据集文件的路径。如果数据集有多个拆分(如训练集和测试集),可以使用字典格式来指定每个拆分的文件路径。python # 对于单个文件 dataset = load_dataset('csv', data_f
dataset = load_dataset('csv', data_files={'train':['my_train_file_1.csv','my_train_file_2.csv'],'test':'my_test_file.csv'}) 2.2.2 加载图片 如下我们通过打开指定图片目录进行加载图片数据集 dataset = load_dataset(path="imagefolder", ...
Describe the bug When I load a dataset from a number of arrow files, as in: random_dataset = load_dataset( "arrow", data_files={split: shard_filepaths}, streaming=True, split=split, ) I'm able to get fast iteration speeds when iterating ...
What is the structure of your JSON files. Please note that it is normally simpler if the data file format is JSON-Lines instead. BUAADreamer commented Jun 10, 2023 Thanks for reporting, @cjt222. What is the structure of your JSON files. Please note that it is normally simpler if the ...
data_files = {"train": "train.csv", "test": "test.csv"} dataset = load_dataset("namespace/your_dataset_name", data_files=data_files) 如果不指定使用哪些数据文件,load_dataset将返回所有数据文件。 使用data_files参数加载文件的特定子集: from datasets import load_dataset c4_subset = load_dat...
dataset = load_dataset('text', data_files={'train': ['my_text_1.txt', 'my_text_2.txt'], 'test': 'my_test_file.txt'}) 1.2 加载远程数据集 url = "https://github.com/crux82/squad-it/raw/master/" data_files = { "train": url + "SQuAD_it-train.json.gz", ...
替换为您的文件路径all_files=glob.glob(path+"*.json")# 创建一个空的列表来存储数据框dataframes=[]forfileinall_files:# 读取 JSON 文件并添加到列表中df=pd.read_json(file)dataframes.append(df)# 合并所有数据框combined_df=pd.concat(dataframes,ignore_index=True)# 显示合并后的数据框print(...
['id', 'title', 'context', 'question', 'answers'], num_rows: 87599 }), 'validation': Dataset({ features: ['id', 'title', 'context', 'question', 'answers'], num_rows: 10570 }) }) """ # 2.加载本地存储的 CSV 文件 dataset = load_dataset("csv", data_files="path_to_your...
tfds.load()和tf.data.Dataset的简介 tfds.load()有以下参数 tfds.load( name, split=None, data_dir=None, batch_size=None, shuffle_files=False, download=True, as_supervised=False, decoders=None, read_config=None, with_info=False, builder_kwargs=None, download_and_prepare_kwargs=None, ...
664 664 dataset_7M = load_dataset("parquet", data_files=data_files_7M, split="train").remove_columns(["id"]) 665 665 dataset_Gen = load_dataset("parquet", data_files=data_files_Gen, split="train").remove_columns(["id"]) 666 666 dataset = concatenate_datasets([dataset_7M, datas...