The dataset viewer is not available for this dataset.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
jwt.exceptions.InvalidSignatureError: Signature verification failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Stratified and Nested Subsets of AG News for Performance Benchmarking
Dataset Summary
This repository contains stratified and progressively smaller, nested subsets of the AG News dataset. It was specifically created to benchmark the performance (e.g., accuracy, training time, and resource usage) of fine-tuning language models on varying amounts of training data.
By using stratified samples, each training subset maintains the original class distribution of the AG News dataset, ensuring that performance differences can be more reliably attributed to dataset size rather than class imbalance.
The splits are nested, meaning train_very_very_small is a perfect subset of train_very_small, which is a subset of train_small, and so on.
Supported Tasks and Leaderboards
text-classification: The dataset can be used to train models for news topic classification. The four classes are: World (0), Sports (1), Business (2), and Sci/Tech (3).
Dataset Structure
The dataset contains six splits. A test split was created from 20% of the original data. The remaining 80% forms the train_full split, from which all other training sets were derived.
| Split Name | Number of Samples | Description |
|---|---|---|
test |
24,000 | 20% of the original data, for evaluation. |
train_full |
96,000 | 80% of the original data (full training set). |
train_medium |
48,000 | 1/2 of the train_full set. |
train_small |
24,000 | 1/4 of the train_full set. |
train_very_small |
12,000 | 1/8 of the train_full set. |
train_very_very_small |
6,000 | 1/16 of the train_full set. |
Data Fields
text: astringfeature.label: a classification label, with possible values includingWorld(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_test"
dataset = load_dataset(repo_name)
# You can now access any split
print("Medium training set:")
print(dataset['train_medium'])
print("\nTest set:")
print(dataset['test'])
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}
}
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