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: 加载一个数据集: python dataset = ...
1. 安装datasets库 在终端中运行以下命令来安装datasets库: ```bash pip install datasets ``` 2. 从datasets模块中导入load_dataset方法 在你的Python脚本或Jupyter笔记本中,使用以下代码导入load_dataset方法: ```python from datasets import load_dataset ``` 这一步将允许你使用load_dataset方法来加载数据集。
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...
# This script needs these libraries to be installed: # numpy, transformers, datasets import wandb import os import numpy as np from datasets import load_dataset from transformers import TrainingArguments, Trainer from transformers import AutoTokenizer, AutoModelForSequenceClassification def tokenize_functio...
importdatasetsfromrenumicsimportspotlightds=datasets.load_dataset('renumics/emodb-enriched',split='all')layout=spotlight.layouts.debug_classification(label='gender',prediction='m1_gender_prediction',embedding='m1_embedding',features=['age','emotion'])spotlight.show(ds,layout=layout) ...
from tensorflow.keras.datasetsimportmnist # 加载MNIST数据集(x_train,y_train),(x_test,y_test)=mnist.load_data()# 数据预处理 x_train=x_train/255.0x_test=x_test/255.0# 创建数据集对象 train_dataset=tf.data.Dataset.from_tensor_slices((x_train,y_train))test_dataset=tf.data.Dataset.from_te...
1. Only Explore admins have access to the Dataset Exports feature. This point is added to this article now.2. All Explore admins have access to all active and recent exports. So, the access is per account. 3. There is no way to create the export on someone's behalf but any admin ...
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: # ...
importosfromtransformersimportTrainingArgumentsfromdatasetsimportload_datasetfromtrlimportSFTTrainerfrompeftimportLoraConfigdataset=load_dataset("imdb",split="train")output_dir="test"training_args=TrainingArguments(output_dir=output_dir,per_device_train_batch_size=1,per_device_eval_batch_size=1,max_steps=...