load_dataset有以下参数,具体可参考源码 defload_dataset( path: str, name: Optional[str] = None, data_dir: Optional[str] = None, data_files: Union[Dict, List] = None, split: Optional[Union[str, Split]] = None, cache_dir: Optional[str] = None, ...
load_dataset函数加载本地数据集,可以按照以下步骤进行: 确定数据集的本地存储路径: 确保你的本地数据集文件已经准备好,并且你知道它们的存储路径。例如,假设你有一个CSV格式的数据集文件,存储路径为./data/my_dataset.csv。 导入load_dataset函数所在的库: 在Python脚本或Jupyter Notebook中导入datasets库,并确保你...
在使用魔塔的 MsDataset.load()方法加载某个数据集时,指定了cache_dir,这样第一次会自动从远程下载数据集到本地路径。问题是在之后使用数据集的过程中,为何还会默认从远程下载呢?该如何从本地读取已经下载好了的数据?不知道是哪里需要修改,按道理应该首先读取本地缓存,毕竟大数据集下载太麻烦了。1992871360206904 202...
_dataset_path, self._dataset_name, split=self._split) else: data = load_dataset(os.path.join(self._dataset_path,'wikitext'), self._dataset_name, split=self._split, cache_dir="/home/chenyidong/tmp") _dataset_name = 'wikitext-2-raw-v1' _dataset_path = self._dataset_path if ...
dataset = datasets.load_dataset("monash_tsf", "traffic_hourly", cache_dir="./hf_cache", download_config=config ) 2.2 Transformer使用报错 报错代码: from transformers import pipline 经典报错:Failed to import transformers.pipelines because of the following error (look up to see its traceback): ...
你好,在使用自定义数据集(与示例数据集一致)时,按照示例Config文件进行运行,在load_dataset函数有报错,具体信息如下: Traceback (most recent call last): File "/opt/anaconda3/envs/forRL/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 901,
hfdataset = load_dataset(path, name=name, **kwargs)其中 path=D:\code_for_python\Adaseq\Ada...
from petastorm.spark import SparkDatasetConverter, make_spark_converter spark.conf.set(SparkDatasetConverter.PARENT_CACHE_DIR_URL_CONF, 'file:///dbfs/...') You can either explicitly delete the cache after using it by calling converter.delete() or manage the cache implicitly by configuring the ...
lines= file.readlines(100000)# 方案2.通过预读,达到cache的效果 ifnotlines:breakforlineinlines:pass#do something yield处理大数据? 在python中 比如读取一个500G文件大小,如果使用readLines()方法和read()方法都是不可取的这样的话,直接会导致内存溢出,比较好的方法是使用read(limitSize)或 readLine(limitSize)方...
>>> from datasets import load_dataset >>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train'], 'validation': ['/ssd/datasets/imagenet/pytorch/val']} >>> ds = load_dataset('nateraw/image-folder', data_files=data_files, cache_dir='./', task='image-classification') []...