Deep Learning for Classical Japanese Literature
Paper • 1812.01718 • Published
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: RuntimeError
Message: Dataset scripts are no longer supported, but found kmnist.py
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1167, in dataset_module_factory
raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
RuntimeError: Dataset scripts are no longer supported, but found kmnist.pyNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for KMNIST
Dataset Details
Dataset Description
This dataset contains two variants, Kuzushiji-MNIST and Kuzushiji-49.
Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset.
Kuzushiji-49, as the name suggests, has 49 classes, is a much larger, but imbalanced dataset containing 48 Hiragana characters and one Hiragana iteration mark.
- License: CC BY-SA 4.0
Dataset Sources
- Homepage: https://github.com/rois-codh/kmnist
- Paper: Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., & Ha, D. (2018). Deep learning for classical japanese literature. arXiv preprint arXiv:1812.01718.
Dataset Structure
Kuzushiji-MNIST:
Total images: 70,000
Classes: 10 categories
Splits:
Train: 60,000 images
Test: 10,000 images
Image specs: 28×28 pixels, grayscale
Kuzushiji-49:
Total images: 270,912
Classes: 49 categories
Splits:
Train: 232,365 images
Test: 38,547 images
Image specs: 28×28 pixels, grayscale
Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("randall-lab/kmnist", name="kmnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/kmnist", name="kmnist", split="test", trust_remote_code=True)
# dataset = load_dataset("randall-lab/kmnist", name="k49mnist", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/kmnist", name="k49mnist", split="test", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
label = example["label"]
image.show() # Display the image
print(f"Label: {label}")
Citation
BibTeX:
@article{clanuwat2018deep, title={Deep learning for classical japanese literature}, author={Clanuwat, Tarin and Bober-Irizar, Mikel and Kitamoto, Asanobu and Lamb, Alex and Yamamoto, Kazuaki and Ha, David}, journal={arXiv preprint arXiv:1812.01718}, year={2018} }
- Downloads last month
- 120