一、Load dataset 1.1 Hugging Face Hub 1.2 本地和远程文件 1.2.1 CSV 1.2.2 JSON 1.2.3 text 1.2.4 Parquet 1.2.5 内存数据(python字典和DataFrame) 1.2.6 Offline离线(见原文) 1.3 切片拆分(Slice splits) 1.3.1 字符串拆分(包括交叉验证) 1.4 Troubleshooting故障排除 1.4.1手动下载 1.4.2 Specify fe...
dataset=load_dataset("path/to/script/loading_script.py",split="train") 编辑加载代码 你可以编辑一个加载数据集代码,下载数据集的代码仓库,然后修改,最后加载。 git clone https://huggingface.co/datasets/eli5fromdatasetsimportload_dataset eli5=load_dataset("path/to/local/eli5") 本地和远程文件 数据集...
使用datasets加载数据集非常简单,只需调用load_dataset函数并传入相应的参数即可。参数可以是HuggingFace Hub上的数据集存储库命名空间和数据集名称,也可以是本地磁盘上的数据集文件路径。加载完成后,将返回一个数据集对象,我们可以对其进行进一步的处理和查询。 例如,加载HuggingFace Hub上的数据集: from datasets import ...
下载: huggingface-cli download your-dataset --local-dir path 加载: 从path里面找到你的所有数据文件, 不妨记作xxx.parquet load_datasets('parquet', data_files={'train':'path/xxx.parquet','test':other-files}) 换句话说你得根据你下载的数据集的readme手动去把数据找出来=v=发布...
You can load a csv data file from local path using: from datasets import load_dataset dataset = load_dataset('csv', data_files='final.csv') or to load multiple files, use: dataset = load_dataset('csv', data_files={'train' ['my_train_file_1.csv', 'my_train_file_2.csv'], '...
I'm trying to load a custom dataset to use for finetuning a Huggingface model. My data is a csv file with 2 columns: one is 'sequence' which is a string , the other one is 'label' which is also a string, with 8 classes. I want to load my dataset and assign the typ...
# Stream from the internetmy_iterable_dataset=load_dataset("c4","en",split="train",streaming=True)my_iterable_dataset.n_shards# 1024# Stream from local filesdata_files={"train":[f"path/to/data_{i}.csv"foriinrange(1024)]}my_iterable_dataset=load_dataset("csv",data_files=data_files,...
可以通过设置TRANSFORMERS_CACHE环境变量控制模型的保存路径,详情见 HelloWorld:huggingface 模型下载与离线...
对于NLP 爱好者来说HuggingFace肯定不会陌生,因为现在几乎一提到NLP就会有HuggingFace的名字出现,HuggingFace...
When i used the datasets==1.11.0, it's all right. Util update the latest version, it get the error like this: >>> from datasets import load_dataset >>> data_files={'train': ['/ssd/datasets/imagenet/pytorch/train'], 'validation': ['/ssd/d...