muhammadravi251001/debug-entailment

该数据集可能用于自然语言推理（NLI）任务，包含前提（premise）、假设（hypothesis）和标签（label）等列。数据集分为训练集、验证集和测试集，并且提供了保留无效数据和删除无效数据的两种版本。

原始信息汇总

数据集概述

数据集下载方法

仅下载特定列（premise, hypothesis, label）

python from datasets import load_dataset, Dataset, DatasetDict import pandas as pd

data_files = {"train": "data_nli_train_df_debug.csv", "validation": "data_nli_val_df_debug.csv", "test": "data_nli_test_df_debug.csv"}

dataset = load_dataset("muhammadravi251001/debug-entailment", data_files=data_files)

selected_columns = ["premise", "hypothesis", "label"]

df_train = pd.DataFrame(dataset["train"]) df_train = df_train[selected_columns]

df_val = pd.DataFrame(dataset["validation"]) df_val = df_val[selected_columns]

df_test = pd.DataFrame(dataset["test"]) df_test = df_test[selected_columns]

train_dataset = Dataset.from_dict(df_train) validation_dataset = Dataset.from_dict(df_val) test_dataset = Dataset.from_dict(df_test)

dataset = DatasetDict({"train": train_dataset, "validation": validation_dataset, "test": test_dataset})

保留无效数据的数据集

python from datasets import load_dataset, Dataset, DatasetDict import pandas as pd

data_files = {"train": "data_nli_train_df_keep.csv", "validation": "data_nli_val_df_keep.csv", "test": "data_nli_test_df_keep.csv"}

dataset = load_dataset("muhammadravi251001/debug-entailment", data_files=data_files)

selected_columns = dataset.column_names[train]

df_train = pd.DataFrame(dataset["train"]) df_train = df_train[selected_columns]

df_val = pd.DataFrame(dataset["validation"]) df_val = df_val[selected_columns]

df_test = pd.DataFrame(dataset["test"]) df_test = df_test[selected_columns]

train_dataset = Dataset.from_dict(df_train) validation_dataset = Dataset.from_dict(df_val) test_dataset = Dataset.from_dict(df_test)

dataset = DatasetDict({"train": train_dataset, "validation": validation_dataset, "test": test_dataset})

删除无效数据的数据集

python from datasets import load_dataset, Dataset, DatasetDict import pandas as pd

data_files = {"train": "data_nli_train_df_drop.csv", "validation": "data_nli_val_df_drop.csv", "test": "data_nli_test_df_drop.csv"}

dataset = load_dataset("muhammadravi251001/debug-entailment", data_files=data_files)

selected_columns = dataset.column_names[train]

df_train = pd.DataFrame(dataset["train"]) df_train = df_train[selected_columns]

df_val = pd.DataFrame(dataset["validation"]) df_val = df_val[selected_columns]

df_test = pd.DataFrame(dataset["test"]) df_test = df_test[selected_columns]

train_dataset = Dataset.from_dict(df_train) validation_dataset = Dataset.from_dict(df_val) test_dataset = Dataset.from_dict(df_test)

dataset = DatasetDict({"train": train_dataset, "validation": validation_dataset, "test": test_dataset})