metadata
language:
- en
- fr
license: mit
task_categories:
- text-generation
- translation
pretty_name: Keep it simple !
size_categories:
- 100K<n<1M
keep-it-simple
Objective: An ultra-minimalist dataset for pre-training tiny language models. The logic relies on bidirectional symmetry (A is B and B is A]) to foster deep semantic understanding. By training the model to predict the "prompt" from the "text" and vice versa, we maximize the utility of every pair.
Data Sources
- Simple English Wikipedia: Simplified encyclopedic articles.
- Vikidia (FR): Educational content for younger audiences.
- OPUS Books (en-fr): Aligned English-French literary translations.
- Cosmopedia-100k: Synthetic educational content.
Structure
| Column | Description |
|---|---|
prompt |
Input (concept, title, or English translation). |
text |
Output (explanation, summary, or French translation). |
seed_data |
Origin identifier (traceability). |
Context & Usage
- Bidirectional Training: Each source item yields two training entries (
prompt$\rightarrow$textandtext$\rightarrow$prompt). This enforces semantic symmetry, reversal curve and limitate span corruption. - Minimalism: More compact than the BabyLM challenge; focused on density and the purity of pairs to maximize efficiency on tiny, resource-constrained architectures.
- Goal: Rapid testing of alignment theories and training "pocket" models for fundamental, bidirectional interactions.
This dataset is a minimalist research tool.