gramajo/nouns-proposal-predictor

Nouns DAO Proposal Predictor

A DistilBERT model fine-tuned on Nouns DAO Proposals to predict whether a governance proposal will pass or fail based on its title and description.

Model Description

• Model: distilbert-base-uncased (66M parameters)
• Task: Binary text classification (Pass / Fail)
• Training data: 785 Nouns DAO proposals (chronological train split)
• Evaluation data: 197 proposals (most recent 20%, chronological test split)
• Training hardware: Google Colab T4 GPU (~5 minutes)

Performance

Evaluated on the chronological test set (proposals 786–982, where the DAO was significantly more conservative):

Metric	Score
Accuracy	~58%
F1 (binary)	~0.42
Precision	~0.35
Recall	~0.54

⚠️ Important context: The test set has a 28% pass rate vs 56% in the training set due to the Nouns DAO becoming more conservative over time. This chronological split is intentional — it simulates real-world prediction of future proposals from historical data. A random split would show inflated ~65% F1 but would leak temporal information.

Intended Use

• Educational tool for understanding DAO governance patterns
• Baseline model for proposal outcome prediction research
• Not intended for: automated voting, financial decisions, or production governance without human oversight

Limitations

• Trained on only 785 examples — a very small dataset by ML standards
• The Nouns DAO's voting patterns, participants, and norms have shifted significantly over 5 years
• Proposal descriptions vary wildly in length and format (some have images, markdown, etc.)
• Categories (Art, Governance, Funding, Events, Infrastructure) are LLM-assigned, not human-verified
• Does not use proposal category as a feature (text-only v1)

Training Details

• Epochs: 4 (with early stopping)
• Batch size: 16
• Learning rate: 2e-5
• Max sequence length: 512 tokens
• Class weights: Computed from training set imbalance (56%/44% pass/fail)
• Loss: Weighted cross-entropy

Dataset

Trained on gramajo/nouns-proposals — all 983 Nouns DAO proposals from August 2021 to July 2026, with pass/fail labels and LLM-assigned categories.

Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="gramajo/nouns-proposal-predictor")

result = classifier("Your proposal title and description here")
# [{'label': 'LABEL_1', 'score': 0.73}]  — LABEL_1 = Pass, LABEL_0 = Fail

Citation

@misc{nouns-proposal-predictor-2026,
  author = {Juan Gramajo},
  title = {Nouns DAO Proposal Predictor},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\\url{https://huggingface.co/gramajo/nouns-proposal-predictor}}
}

License

MIT. Built on Nouns DAO data which is CC0 (public domain).