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...
dataset = Dataset.from_dict(my_dict) # 从dataFrame导入数据 import pandas as pd df = pd.DataFrame({"a": [1, 2, 3]}) dataset = Dataset.from_pandas(df) 1.4数据切片 加载完数据之后我们看看有那些内容,简单两行代码导入数据,然后打印出来看一下; from datasets import load_datasetdatasets= load_...
importos os.environ["HF_ENDPOINT"]="https://hf-mirror.com"fromdatasetsimportload_dataset dataset=load_dataset(path='squad',split='train')print(dataset) 因为原网址是不可用的,如图 hf 原网址 上面修改的环境变量是在 datasets 库中的 config.py 文件中的变量,如下图: 环境变量...
# 需要导入模块: import dataset [as 别名]# 或者: from dataset importDatasetFromHdf5[as 别名]defmain():globalopt, model opt = parser.parse_args() print(opt) cuda = opt.cudaifcudaandnottorch.cuda.is_available():raiseException("No GPU found, please run without --cuda") opt.seed = random...
Our dataset is small; it contains only a few thousand records. It is a great dataset to use because we will not run into performance problems. If your dataset is larger, check out âWorking with Large Datasetsâ for options. Working with Large Datasets Donât sta...
datasets, weights=None, seed=None, stop_on_empty_dataset=False) 参数 datasets具有兼容结构的tf.data.Dataset对象的非空列表。 weights(可选。)len(datasets)浮点值的列表或张量,其中weights[i]表示从datasets[i]或tf.data.Dataset对象中采样的概率,其中每个元素都是这样的列表。默认为跨datasets的均匀分布。
I have 2 datasets (df1 and df2), one with a time interval (df1) and one with precipitation data (df2). I would like to get the total amount of precipitation for the time interval in df1. Because of all of the other data in df1 I cannot combine the 2 datasets, ...
import pandas as pd df = pd.read_json(jsonl_path, lines=True) df.head() from datasets import Dataset dataset = Dataset.from_pandas(df) 加载后的dataset也能使用,但后续用dataset.map进行处理也会非常慢。 高效解决方案 一种方法是先将jsonl文件转换成arrow格式,然后使用load_from_disk进行加载: # ...
Error: ImportError: cannot import name 'build_dataset' from 'mmdet.datasets' My environment was set up with the following installations: Torch version: 2.0.0 with CUDA support MMDetection: 3.0.0 MMCV: 2.0.0 MMEngine: 0.7.3 Given that this issue has persisted for over a month without a res...
acc= model.eval(ds_eval, dataset_sink_mode=False)print("{}".format(acc)) mnist_path="./datasets/MNIST_Data"train_epoch= 1dataset_size= 1model= Model(net, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) train_net(args, model, train_epoch, mnist_path, dataset_size, ckpoint,...