pip install datasets 导入Dataset和DatasetDict类: 在你的Python脚本或Jupyter Notebook中,使用以下代码来导入Dataset和DatasetDict类: python from datasets import Dataset, DatasetDict 使用Dataset和DatasetDict: 一旦导入,你就可以使用这些类来加载、
from datasets import load_datasetsquad_it_dataset= load_dataset("json", data_files="./data/SQuAD_it-train.json", field="data") #也可以加载文本文件 dataset = load_dataset('text', data_files={'train': ['my_text_1.txt', 'my_text_2.txt'], 'test': 'my_test_file.txt'}) 1.2 加...
from datasets import load_dataset dataset = load_dataset("squad", split="train") dataset.features {'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None), 'context': Value(dtype='string', id=None...
Steps to reproduce the bug fromdatasetsimportload_datasetdataset=load_dataset("art")dataset.save_to_disk("mydir")d=Dataset.load_from_disk("mydir") Expected results It is expected that these two functions be the reverse of each other without more manipulation ...
You can directly load our data using datasets and load our model using transformers. # load our dataset from datasets import load_dataset iterater_dataset = load_dataset("wanyu/IteraTeR_human_sent") iterater_plus_multi_sent_dataset = load_dataset("zaemyung/IteraTeR_plus", "multi_sent") # ...
import datasets import evaluate import torch from datasets import load_dataset from peft import ( LoraConfig, PeftModel, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, prepare_model_for_kbit_training, ...
class_names = image_datasets['train'].classes (4)读取标签对应的实际名字 cat_to_name.json文件中保存了每一个序号对应的花的名字。 withopen('cat_to_name.json','r')asf: cat_to_name = json.load(f) 部分文件内容: (5)展示数据 数据展示需要将tensor的数据需要转换成numpy的格式,而且还需要还原回...
mac2id = dict() #mac2id是一个字典:key是mac地址value是对应mac地址的上网时长以及开始上网时间 onlinetimes = [] #value:对应mac地址的上网时长以及开始上网时间 f = open('TestData.txt', encoding='utf-8') for line in f: mac = line.split(',')[2] #读取每条数据中的mac地址 ...
[-1],'.txt')forxinself.img_files]#加载全部的.txt# Check cache #可以设置缓存,再训练就不用一个个读了cache_path =str(Path(self.label_files[0]).parent) +'.cache'# 缓存文件cached labelsifos.path.isfile(cache_path):#如果有缓存cache = torch.load(cache_path)# 直接读缓存loadifcache['...
My own task or dataset (give details below) Reproduction fromdatasetsimportload_dataset,Features,Value,ClassLabelclass_names=["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue",...