| license: mit | |
| task_categories: | |
| - other | |
| tags: | |
| - ai-models | |
| - training-data | |
| - tokens | |
| - machine-learning | |
| size_categories: | |
| - n<1K | |
| # Training Data Scale Registry | |
| A systematic registry of AI model training data size estimates with evidence profiles. | |
| ## Dataset Description | |
| This dataset contains structured records of AI models with: | |
| - Token count estimates (min/max/mid) | |
| - Evidence types (E1-E5) and strength (S-High/Medium/Low) | |
| - Uncertainty sources (U1-U5) | |
| - Model metadata (parameters, FLOPs, architecture) | |
| - Raw evidence snippets | |
| ## Data Collection | |
| Data is collected from: | |
| - Epoch AI datasets | |
| - Hugging Face model cards | |
| - Technical reports and system cards | |
| - Third-party analyses | |
| ## Inference Methods | |
| Token estimates are derived using: | |
| - Chinchilla scaling law | |
| - Hardware back-calculation | |
| - Parameter ratio heuristics | |
| - Textual token clues | |
| - Third-party analyses | |
| ## Evidence Profiles | |
| Each model includes an evidence profile indicating: | |
| - **Evidence Types**: How the estimate was derived | |
| - **Evidence Strength**: Confidence in the estimate | |
| - **Uncertainty Sources**: What information is missing | |
| ## Usage | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset("midah/odl-training-data") | |
| ``` | |
| ## Citation | |
| If you use this dataset, please cite: | |
| ``` | |
| Training Data Scale Registry | |
| ODL Research | |
| 2025 | |
| ``` | |