| --- |
| license: mit |
| task_categories: |
| - question-answering |
| language: |
| - en |
| tags: |
| - sunbird |
| - sunbirdai |
| pretty_name: Sunbird Salt Documentation |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: chunks/*.json |
| --- |
| |
| # COCIS WEB INFO |
|
|
| ## Dataset Summary |
| This dataset contains text chucks scraped from its official website and corresponding websites. |
| The dataset consists of **JSON chunks**, designed for high-performance streaming and parallel processing. Each chunk represents a discrete unit of data structured for machine learning tasks. |
|
|
| By sharding the data into chuck files, this repository supports the `datasets` library's streaming mode, allowing users to train models without downloading the entire dataset into RAM—a critical feature for resource-constrained environments or high-concurrency CI/CD pipelines. |
|
|
| ## Repository Structure |
| The data is organized into a `chunks/` directory to maintain a clean root level: |
|
|
| ```text |
| . |
| ├── README.md # This file |
| └── chunks/ # Directory containing JSON files |
| ├── chunk_1.json |
| ├── chunk_2.json |
| └── ... |
| ``` |
|
|
| ## Usage |
|
|
| You can load this dataset directly using the Hugging Face datasets library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Standard loading |
| dataset = load_dataset("jimjunior/sunbird_salt_docs") |
| |
| # Streaming mode (Recommended for many shards) |
| streamed_dataset = load_dataset("jimjunior/sunbird_salt_docs", streaming=True) |
| print(next(iter(streamed_dataset["train"]))) |
| ``` |
|
|
| ## Maintenance and Contributions |
|
|
| This dataset was created as part of the Sunbird AI Internship 2026. Its actively mantained by [Beingana Jim Junior](https://www.linkedin.com/in/jim-junior-beingana/). |
|
|
