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、处理和管理数据集了。以下是一些基本的使用示例: 加载一个数据...
from datasets import load_dataset , Dataset datasets = load_dataset('cail2018') # 导入数据 datasets_sample = datasets[ "exercise_contest_train" ].shuffle(seed= 42 ).select( range ( 1000 )) datasets_sample = datasets_sample.sort('punish_of_money') # 按照被罚金额排序,是从大到小的,这个排...
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...
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 Actual results ...
utils import gather_object, broadcast from datasets import load_dataset from rich.console import Console from rich.pretty import pprint from rich.table import Table from torch import optim from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter...
from datasets import load_dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, pipeline, logging, ) from peft import LoraConfig, PeftModel from trl import SFTTrainer 我们继续分析导入 torch是我们很熟悉的深度学习库,这里我们不需要torch的那些低级功...
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 ...
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...
'dataset', 'delete_dataset', 'delete_table', 'extract_table', 'from_service_account_json', 'get_dataset', 'get_job', 'get_service_account_email', 'get_table', 'insert_rows', 'insert_rows_json', 'job_from_resource', 'list_datasets', ...
We uploaded both our datasets and model checkpoints to Hugging Face'srepo. You can directly load our data usingdatasetsand load our model usingtransformers. # load our datasetfromdatasetsimportload_datasetiterater_dataset=load_dataset("wanyu/IteraTeR_human_sent")iterater_plus_multi_sent_dataset=load...