Datasets:

szhuggingface
/

ag_news

dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': World
            '1': Sports
            '2': Business
            '3': Sci/Tech
  splits:
    - name: test
      num_bytes: 1879474
      num_examples: 7600
    - name: val_full
      num_bytes: 5963460.6
      num_examples: 24000
    - name: val1
      num_bytes: 2981730.3
      num_examples: 12000
    - name: val2
      num_bytes: 2981730.3
      num_examples: 12000
    - name: train_full
      num_bytes: 23853842.4
      num_examples: 96000
    - name: train_1_48k
      num_bytes: 11926921.2
      num_examples: 48000
    - name: train_1_24k
      num_bytes: 5963460.6
      num_examples: 24000
    - name: train_1_12k
      num_bytes: 2981730.3
      num_examples: 12000
    - name: train_1_6k
      num_bytes: 1490865.15
      num_examples: 6000
    - name: train_1_3k
      num_bytes: 745432.575
      num_examples: 3000
    - name: train_1_1.5k
      num_bytes: 372716.2875
      num_examples: 1500
  download_size: 41027925
  dataset_size: 61141363.7125
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
      - split: val_full
        path: data/val_full-*
      - split: val1
        path: data/val1-*
      - split: val2
        path: data/val2-*
      - split: train_full
        path: data/train_full-*
      - split: train_1_48k
        path: data/train_1_48k-*
      - split: train_1_24k
        path: data/train_1_24k-*
      - split: train_1_12k
        path: data/train_1_12k-*
      - split: train_1_6k
        path: data/train_1_6k-*
      - split: train_1_3k
        path: data/train_1_3k-*
      - split: train_1_1.5k
        path: data/train_1_1.5k-*
pretty_name: Stratified and Nested Subsets of AG News for Benchmarking
tags:
  - ag_news
  - text-classification
  - stratified-sampling
  - low-resource
  - benchmarking
license: apache-2.0

Stratified and Nested Subsets of AG News for Performance Benchmarking

Dataset Summary

This repository contains a script to generate stratified and progressively smaller, nested subsets of the AG News dataset. It was specifically designed to benchmark the performance (e.g., accuracy, training time, and resource usage) of language models on varying amounts of training data.

This new version improves the benchmarking process by:

Preserving the Original Test Set: It uses the original ag_news test set for final, unbiased evaluation.
Creating Dedicated Validation Sets: It carves out multiple validation sets (val_full, val1, val2) from the original training data, allowing for robust hyperparameter tuning without touching the test set.

By using stratified samples, each subset maintains the original class distribution of the AG News dataset. This ensures that performance differences can be more reliably attributed to dataset size rather than class imbalance. The training splits are nested, meaning train_1_1.5k is a perfect subset of train_1_3k, which is a subset of train_1_6k, and so on.

Supported Tasks and Leaderboards

text-classification: The dataset is ideal for training models for news topic classification. The four classes are: World (0), Sports (1), Business (2), and Sci/Tech (3).

Dataset Structure

The dataset generation script creates multiple splits organized into three categories: training, validation, and testing.

The test split is the original, untouched test set from the ag_news dataset.
The validation splits are derived from 20% of the original ag_news training data.
The training splits are progressively smaller, nested subsets derived from the remaining 80% of the original ag_news training data.

Category	Split Name	Number of Samples	Description
Test	`test`	7,600	The original `ag_news` test set. Use for final evaluation.
Validation	`val_full`	24,000	20% of the original training data.
	`val1`	12,000	A 50% split of `val_full`.
	`val2`	12,000	The other 50% split of `val_full`.
Training	`train_full`	96,000	80% of the original training data (full training set).
	`train_1_48k`	48,000	1/2 of `train_full`.
	`train_1_24k`	24,000	1/4 of `train_full`.
	`train_1_12k`	12,000	1/8 of `train_full`.
	`train_1_6k`	6,000	1/16 of `train_full`.
	`train_1_3k`	3,000	1/32 of `train_full`.
	`train_1_1.5k`	1,500	1/64 of `train_full`.

Data Fields

text: a string feature containing the news article content.
label: a classification label, with possible values including World (0), Sports (1), Business (2), Sci/Tech (3).

How to Use

You can load the dataset using the datasets library with a single line of code:

from datasets import load_dataset
repo_name = "szhuggingface/ag_news" 
dataset = load_dataset(repo_name)

# You can now access any split
print("Medium training set:")
print(dataset['train_1_6k'])

print("\nTest set:")
print(dataset['test'])

Instead of loading pre-made splits from the Hub, this repository provides a script to generate the datasets locally. This ensures transparency and allows for easy modification of the splitting parameters (like random_state).

Save the code below as a Python file (e.g., create_splits.py) and run it.

import pandas as pd
from datasets import load_dataset, DatasetDict

def create_stratified_splits(
    dataset_name="ag_news",
    label_col="label",
    validation_size=0.2,
    random_state=42
):
    """
    Loads the ag_news train and test splits. It creates multiple validation
    and training sets for benchmarking purposes.

    - The final 'test' set is the original ag_news test set.
    - Validation sets ('val_full', 'val1', 'val2') are derived from the original train set.
    - Training sets are nested, stratified subsets of the remaining training data.

    Args:
        dataset_name (str): The name of the dataset on the Hugging Face Hub.
        label_col (str): The name of the column to stratify by.
        validation_size (float): The proportion of the original train set to use for validation.
        random_state (int): The seed for random operations to ensure reproducibility.

    Returns:
        DatasetDict: A dictionary containing all the created dataset splits.
    """
    print(f"▶️ Loading original '{dataset_name}' train and test splits...")
    original_train_ds = load_dataset(dataset_name, split='train')
    original_test_ds = load_dataset(dataset_name, split='test')

    # 1. Create the main Train/Validation stratified split from the ORIGINAL TRAIN data
    print(f"\nSplitting original training data into a main training set ({1-validation_size:.0%}) and a full validation set ({validation_size:.0%})...")
    main_splits = original_train_ds.train_test_split(
        test_size=validation_size,
        stratify_by_column=label_col,
        seed=random_state
    )
    train_full = main_splits['train']
    val_full = main_splits['test']

    # 2. Split the full validation set into two halves
    print("Creating two validation splits (val1, val2) from the full validation set...")
    val_splits = val_full.train_test_split(
        test_size=0.5,
        stratify_by_column=label_col,
        seed=random_state
    )
    val1 = val_splits['train']
    val2 = val_splits['test']

    # 3. Create nested, growing subsets from the main training data
    print("Creating nested training subsets...")
    train_1_48k = train_full.train_test_split(train_size=0.5, stratify_by_column=label_col, seed=random_state)['train']
    train_1_24k = train_1_48k.train_test_split(train_size=0.5, stratify_by_column=label_col, seed=random_state)['train']
    train_1_12k = train_1_24k.train_test_split(train_size=0.5, stratify_by_column=label_col, seed=random_state)['train']
    train_1_6k = train_1_12k.train_test_split(train_size=0.5, stratify_by_column=label_col, seed=random_state)['train']
    train_1_3k = train_1_6k.train_test_split(train_size=0.5, stratify_by_column=label_col, seed=random_state)['train']
    train_1_1_5k = train_1_3k.train_test_split(train_size=0.5, stratify_by_column=label_col, seed=random_state)['train']

    # 4. Consolidate all splits into a single DatasetDict
    final_datasets = DatasetDict({
        'test': original_test_ds,
        'val_full': val_full,
        'val1': val1,
        'val2': val2,
        'train_full': train_full,
        'train_1_48k': train_1_48k,
        'train_1_24k': train_1_24k,
        'train_1_12k': train_1_12k,
        'train_1_6k': train_1_6k,
        'train_1_3k': train_1_3k,
        'train_1_1.5k': train_1_1_5k
    })

    print("\n--- ✅ Final Dataset Statistics ---")
    for name, ds in final_datasets.items():
        print(f"\nDataset Split: '{name}' | Total samples: {len(ds):,}")

    return final_datasets

Citation 📜

If you use this dataset in your work, please cite the original paper:

@inproceedings{zhang2015character,
  title={Character-level convolutional networks for text classification},
  author={Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
  booktitle={Advances in neural information processing systems},
  pages={649--657},
  year={2015}
}