| | --- |
| | license: mit |
| | task_categories: |
| | - text-generation |
| | language: |
| | - en |
| | tags: |
| | - code |
| | - medical |
| | - biology |
| | - chemistry |
| | - finance |
| | --- |
| | |
| |
|
| | # Orion-Spark-2 Dataset |
| |
|
| | ## Overview |
| | The **Orion-Spark-2 Dataset** is a text corpus curated for training the Orion-Spark-2 transformer language model. It consists of a diverse collection of sentences extracted from multiple sources including Wikipedia articles, technology news sites, developer resources, and other open-access web pages. The dataset is designed to provide broad coverage of general knowledge, programming topics, artificial intelligence, space, popular culture, and current events. |
| |
|
| | ## Structure |
| | - **File:** `corpus.txt` |
| | - **Format:** Plain text, one sentence per line. |
| | - **Encoding:** UTF-8 |
| | - **Line Count:** Approximately 60,000+ lines |
| | - **Checkpoint:** `corpus_checkpoint.txt` to track downloaded lines for resuming corpus collection. |
| |
|
| | ## Sources |
| | The dataset draws content from: |
| | - Wikipedia pages (various topics including AI, programming languages, mathematics, astronomy, and historical events) |
| | - News and tech sites (BBC Technology, TechCrunch) |
| | - Open-source repositories (GitHub) |
| | - Educational and community platforms (Fast.ai) |
| | - Hugging Face datasets |
| |
|
| | ## Processing |
| | - Each line in the dataset is cleaned to remove excessive whitespace. |
| | - Sentences shorter than 30 characters are discarded. |
| | - HTML content is parsed using BeautifulSoup to extract text from paragraph and header tags (`<p>`, `<h1>`, `<h2>`, `<h3>`). |
| | - Sentences are split on punctuation marks (`.`, `?`, `!`) to ensure individual sentence granularity. |
| |
|
| | ## Usage |
| | 1. Load the dataset: |
| | ```python |
| | from torch.utils.data import DataLoader |
| | from dataset import TextDataset |
| | dataset = TextDataset(texts, tokenizer) |
| | Use TextDataset for training or evaluation in PyTorch. |
| | |
| | Pad sequences using the collate_batch function when forming batches for model training. |
| | |
| | Notes |
| | The dataset is intended for educational and research purposes. |
| | |
| | It contains only publicly available information; no private or copyrighted content has been included beyond fair use. |
| | |
| | Designed for training medium-sized language models (30M parameters) efficiently with maximum sequence length of 128 tokens. |
