You have done the hard work of collecting, cleaning, and validating your data. The final, most crucial step is to share it with the world. A dataset hidden on a private server is invisible. A dataset made public in a standardized format becomes a building block for the future of AI.
To Preserve a Language, Data Must Be Public
The quality of all future AI models depends on the data they are trained on. By making your corpora public and easily accessible, you ensure that developers and researchers from companies like Google, OpenAI, and Meta can include your language in their next generation of models. Making data public is not just a technical step; it is an act of language preservation.
Why Hugging Face?
Hugging Face is the "GitHub for machine learning." It is the central hub where the entire AI community shares models and datasets. Uploading your data here gives you:
- Maximum Visibility: Everyone in AI looks here first.
- Standardization: Their tools promote best practices for data formatting.
- Free Hosting: They provide free storage for public datasets, including large audio files.
- Integrated Tooling: Your data becomes instantly usable with thousands of open-source tools.
Preparing and Formatting Your Data
Before you upload, your data must be structured in a machine-readable format.
For Text Corpora (Monolingual & Parallel)
The best format is JSON Lines (.jsonl), where each line is a separate JSON object. It's simple, powerful, and easy to process.
Monolingual Corpus (e.g., `corpus.jsonl`):
{"text": "This is the first sentence."}
{"text": "This is the second sentence."}
Parallel Corpus (e.g., `parallel_corpus.jsonl`):
{"translation": {"en": "Hello world.", "fr": "Bonjour le monde."}}
{"translation": {"en": "How are you?", "fr": "Comment ça va?"}}
For Audio Datasets (ASR & TTS)
Audio datasets consist of two parts: a folder of audio files (e.g., .wav, .mp3) and a metadata file that links each audio file to its transcription.
Structure:
my_dataset/
├── audio/
│ ├── recording_001.wav
│ └── recording_002.wav
└── metadata.csv
Metadata File (e.g., `metadata.csv`):
file_name,transcription
audio/recording_001.wav,"This is the first sentence."
audio/recording_002.wav,"This is the second sentence."
The Upload Process
Create a New Dataset
From your profile, click "New" and select "Dataset". Give it a name that clearly identifies the language and content (e.g., `navajo-parallel-corpus`).
Upload Your Files
For small datasets, you can drag-and-drop your files directly in the web interface. For larger datasets (especially with audio), you will need to use Git and Git LFS (Large File Storage). Hugging Face provides excellent step-by-step instructions on your repository page.
Write an Excellent README (Datasheet)
This is the most important part of making your dataset useful. Your `README.md` file should clearly describe:
- What the dataset contains.
- The language and its ISO code (e.g., `en`, `fr`, `nav`).
- Where the data came from (e.g., "scanned from the 1985 Navajo Dictionary").
- The license (we recommend a permissive license like CC-BY-SA 4.0).
- How the data was collected and cleaned.
Need a Faster Way? Our Automation Tool (Paid Service)
Formatting and uploading large datasets can be time-consuming and technical. We offer a paid automation service that handles the entire process for you. We take your raw data, format it correctly for text or audio, create the repository, upload the files using Git LFS, and write a professional datasheet. Contact us for a quote.
Your Data is Live! What's Next?
With your dataset on Hugging Face, you're ready to use the platform's powerful tools to train your own custom language models.
Learn About Training Models