input 是一个有效的 Python 脚本路径 if _has_py_script(args.input): logger.info("loading data from a local python script") raw_datasets = load_dataset( args.input, data_dir='./' if not args.script_data_dir else args.script_data_dir, split=split_flag, num_proc...
这个函数的功能是把数据文件加载到内存,返回DatasetDict 或Dataset,也就是一个Python容器。这个函数中调用的load_dataset是huggingface的datasets库的函数。 def build_dataset(args):"""loading dataset by huggingface"""raw_datasets = Noneif args.handler_name == "LlamaFactoryInstructionHandler":all_datasets = ...
# args.input 是一个有效的 Python 脚本路径 if _has_py_script(args.input): logger.info("loading data from a local python script") raw_datasets = load_dataset( args.input, data_dir='./' if not args.script_data_dir else args.script_data_dir, split=split_flag, num_proc=None if args....
split_flag ="train"load_from_local = os.path.exists(args.input)# 从本地加载ifload_from_local:# args.input 是一个有效的 Python 脚本路径if_has_py_script(args.input): logger.info("loading data from a local python script") raw_datasets = load_dataset( args.input, data_dir='./'ifnotar...
logger.info("loading data from a local python script") raw_datasets = load_dataset( args.input, data_dir='./' if not args.script_data_dir else args.script_data_dir, split=split_flag, num_proc=None if args.strea
mindspeed-llm是昇腾模型套件代码仓,原来叫"modelLink"。这篇文章带大家阅读一下数据处理脚本preprocess_data.py(基于1.0.0分支),数据处理是模型训练的第一步,经常会用到。 文章中贴的源码加了相关注释,同学们可以把源码和注释结合起来看。 首先来看一下main函数 ...
# args.input 是一个有效的 Python 脚本路径 if _has_py_script(args.input): ("loading data from a local python script") raw_datasets = load_dataset( args.input, data_dir='./' if not args.script_data_dir else args.script_data_dir, ...
group.add_argument("--script-data-dir", type=str, default=None, help="Python script dataset direction") def add_tokenizer_args(parser): group = parser.add_argument_group(title='tokenizer') group.add_argument('--tokenizer-type', type=str, default='PretrainedFromHF', choices=['Bert...
Prepare the dataset by downloading "The Stock Exchange" by Charles Duguid from Project Gutenberg: wget https://www.gutenberg.org/ebooks/59042 -O dataset.txt Preprocess the data: python preprocess.py --input dataset.txt --output preprocessed_data.pkl Train the model: python train.py --config ...
Next, we focus on how to prepare the data to convert to the timetable datatype. We then explore the preprocessing functions available with timetables including synchronizing the data sets to a common time reference, assessing data quality, and dealing with duplicate and missing data. At the end...