save_to_disk("path/of/my/dataset/directory") from datasets import load_from_disk reloaded_encoded_dataset = load_from_disk("path/of/my/dataset/directory") 2.6.2 Export导出 文件类型导出方式 CSV datasets.Dataset.to_csv() json datasets.Dataset.to_json() Parquet datasets.Dataset.to_parquet()...
处理完数据集后,您可以使用**save_to_disk()**保存并在以后重用它。 通过提供要保存到的目录的路径来保存数据集: >>> encoded_dataset.save_to_disk("path/of/my/dataset/directory") 使用**load_from_disk()**函数重新加载数据集: >>> from datasets import load_from_disk >>> reloaded_dataset = lo...
Unable to load a dataset from Huggingface that I have just saved. Steps to reproduce the bug On Google colab ! pip install datasets from datasets import load_dataset my_path = "wiki_dataset" dataset = load_dataset('wikipedia', "20200501.fr") dataset.save_to_disk(my_path) dataset = load...
importos.pathfromdatasetsimportload_datasetnow_dir=os.path.dirname(os.path.abspath(__file__))target_dir_path=os.path.join(now_dir,"my_cnn_dailymail")dataset=load_dataset("ccdv/cnn_dailymail",name="3.0.0")dataset.save_to_disk(target_dir_path) ...
如果是save_to_disk,那就是保存到本地的文件中,文件格式如下: 3. 如何加载大数据 nlp的训练中经常要加载超大型的语料,一般情况下占用的内存是加载语料的几倍。这对于性能的牺牲太大,比如gpt-2训练的40G语料,可能会让你的内容爆掉。huggingface设计了两个机制来解决这个问题,第一个是将数据集视为“内存映射”文...
from datasets import load_from_disk processed_datasets.save_to_disk("./news_data") disk_datasets = load_from_disk("./news_data") disk_datasets 加载本地数据集 前面介绍了加载公开数据集并进行处理,但是多数情况下,公开数据集并不能满足我们的需求,需要加载自行准备的数据集。下面来介绍如何加载本地的...
(dataset_dict) def save_shard(shard_idx, save_dir, examples_per_shard): shard_dataset = generate_shard_dataset(examples_per_shard) shard_write_path = Path(save_dir) / f"shard_{shard_idx}" shard_dataset.save_to_disk(shard_write_path) return str(Path(shard_write_path) / "data-00000-...
Would be nice to be able to do data_files=["s3://..."] # or gs:// or any cloud storage path storage_options = {...} load_dataset(..., data_files=data_files, storage_options=storage_options) The idea would be to use fsspec as in download_and_prepare and save_to_disk. This...
if saveDatasetToDisk then torch.save('inverseDataset/trainData', trainData) torch.save('inverseDataset/testData', testData) torch.save('inverseDataset/trainLabels', trainLabels) torch.save('inverseDataset/testLabels', testLabels) end print '==> doing transpose on the data - before sending it...
If you find a way to make it work, please post it here since other users might encounter the same issue. If you don't manage to fix it you can useload_dataseton google colab and then save it usingdataset.save_to_disk("path/to/dataset"). ...