如果你的数据集是一个JSON文件或包含多个拆分(如训练集、验证集和测试集),你可以相应地调整data_files字典。 运行代码,检查数据集是否成功加载: 运行上述代码后,你应该能够看到加载的数据集的结构信息。这通常包括数据集的拆分(如训练集、验证集等)、每个拆分的样本数以及样本的特征等。 处理加载过程中可能出现的...
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 ...
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", ...
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...