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+

README.md

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+---
+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
+```
+

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