hf-dataset-structurer 0.0.3

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Hugging Face Dataset Structurer

Hugging Face Dataset Structurer is a Python wrapper to simplify the process of deploying multi-config datasets to the Hugging Face Hub. We developed this tool because we found the official documentation to be lacking in detail and creating the perception the process requires a manual step to develop a dataset loading script. You don't need to do that! This tool will do it for you.

Installation

pip install -U hf-dataset-structurer

Quickstart

In this example, we will create a bundle for Portuguese NER. Official HuggingFace HAREM dataset entry it's true name is "first-HAREM". We will create a bundle attaching the second-HAREM available here. This dataset has two labelling schemes, DEFAULT and SELECTIVE. Selective is just a coarse-grained version of DEFAULT. These two schemes turn this dataset a good candidate to demonstrate the power of this tool.

from datasets import load_dataset, concatenate_datasets, DatasetDict 
from hf_dataset_structurer.DatasetStructure import DatasetStructure

structurer = DatasetStructure("<<TARGET Hugging Face Dataset Name>>")

# Iterate both Labelling Schemes
for config in ["default", "selective"]:
    # Load Official HAREM Dataset
    primeiro_harem = load_dataset("harem", config)
    
    # Start Structuring Process
    structurer.add_dataset(primeiro_harem['train'], f"primeiro_harem_{config}", split="train")

# Load Second HAREM Datasets

second_harem_default = load_dataset("arubenruben/segundo_harem_default")
second_harem_selective = load_dataset("arubenruben/segundo_harem_selective")

# Notice the function used now is add_dataset_dict. A [DatasetDict](https://huggingface.co/docs/datasets/v2.15.0/en/package_reference/main_classes#datasets.DatasetDict) is a native HuggingFace object that represents a dictionary of datasets.
structurer.add_dataset_dict(second_harem_default, "segundo_harem_default")
structurer.add_dataset_dict(second_harem_selective, "segundo_harem_selective")

# Create Dataset Card to describe the dataset
structurer.attach_dataset_card(
    language="pt",
    license="cc-by-4.0",
    annotations_creators=["expert-generated"],
    task_categories=["token-classification"],
    tasks_ids=["named-entity-recognition"],
    pretty_name="HAREM",
    multilinguality='monolingual'
)

# Push to Hugging Face Hub
structurer.push_to_hub()

API Reference

# Initializes a new instance of the DatasetStructure class.
__init__(self, repo_name: str) -> None

# Accepts a DatasetDict and a config_name and adds it to the dataset structure.
add_dataset_dict(self, dataset_dict: DatasetDict, config_name: str) -> None

# Similar to add_dataset_dict, but accepts a Dataset and a split. Internally, it creates a DatasetDict and calls add_dataset_dict.
add_dataset(self, dataset: Dataset, config_name: str, split: str = "train") -> None

# Attaches a dataset card to the dataset structure.
attach_dataset_card(self, language: str,
                    license: str,
                    annotations_creators: str,
                    task_categories: str,
                    tasks_ids: str,
                    pretty_name: str,
                    multilinguality: str = 'monolingual') -> None

# Pushes the dataset structure and dataset card to the Hugging Face Hub.
push_to_hub(self, private: bool = False) -> None

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT

Acknowledgements

This tool was developed by Ruben Almeida as part of the Project PT-Pump-Up. PT-Pump-Up is a project funded by INESC TEC and the Portuguese Government through the Fundação para a Ciência e a Tecnologia (FCT) that aims to build Portuguese NLP resources and tools to support the development of NLP applications for Portuguese.

References

• HF Official Docs - Structure your repository

• HF Official Docs - Dataset Card