from transformers import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING from IPython.display import clear_outputfrom tqdm importtqdm, trange 自定义数据集加载器: class DatasetRetriever(Dataset): def __init__(self, data, tokenizer, max_len, is_test=False): self.data = data if 'excerpt' in self.data...
比如像图上所示的一个就是Titanic数据集而DS的界面则是囊括了Kaggle上所有的数据集，我们呢可以根据一些条件来搜索，比如说像感兴趣的数据集的类别，像金融、医学、游戏或者体育；或者说我们想做的任务的类型，像回归、分类、NLP或者CV。也可以直接从一些热门的数据集当中进行选择，像图上有一个trending datasets的这...
from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.linear_model import SGDClassifier # 产生数据集 X, Y = datasets.make_classification(n_samples=32000, n_features=30, n_informative=20, n_classes=2) # 划分...
from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split # 构建数据集 X, y = make_classification(n_samples=1000, weights=[0.95], flip_y=0, random_state=1) print(Counter(y)) # 训练集验证集划分 X_train, X_test, y_train, y_test = train_tes...
https://monkeylearn.com/sentiment-analysis/ https://www.kaggle.com/datasets/saurabhshahane/fake-news-classification https://unsplash.com/@obionyeador?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText
校验数据所需要的评价指标,不同的目标函数将会有缺省的评价指标(rmse for regression, and error for classification, mean average precision for ranking) 用户可以添加多种评价指标,对于 Python 用户要以 list 传递参数对给程序,而不是 map 参数 list 参数不会覆盖eval_met...
Here is a short list of some of our favorites that we've already had the chance to review. They're all (mostly) cleaned and ready for analysis! This awesome list is from: https://www.kaggle.com/code/annavictoria/ml-friendly-public-datasets/notebook 数据集 Binary Classification Indian ...
(0.3081, ))])# 可以把传入的图像变成一个数值在0到1之间的张量,这里的均值和标准差都是算好的train_dataset = datasets.MNIST(root='../dataset/mnist/',train=True,download=True,transform=transform)train_loader = DataLoader(train_dataset,shuffle=True,batch_size=batch_size)text_dataset = datasets....
It’s free to join and it gives you the opportunity to practice your skills on real-world datasets in various industries. This post will introduce 10 datasets that are great for practicing your skills…
from sklearn.datasets import make_classification from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import train_test_split from sklearn.ensemble import GradientBoostingClassifier as GBDT fromsklearn.ensemble import ExtraTreesClassifier as ET ...