---
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
```