Hokusai
Explore Models/Litigation Outcome Predictor/Submit Model

Register for Litigation Outcome Predictor

Predicts case outcomes based on structured court data

Legal
Benchmark Requirements
Proposal ID
7
Model Name
Litigation Outcome Predictor
Token Ticker
HLITI
Benchmark Metric
Accuracy
Target / Baseline Value
0.80
Dataset / Test Data Reference
kaggle
Token Reward
1,000 HLITI per verified DeltaOne
Performance target

Reach 0.80 on Accuracy where higher is better.

The proportion of correct predictions

How to Submit Your Model
1

Register Your Model

Use the proposal-owned values below to attach your model registration to the existing proposal through the SDK.

Install SDK

pip install "hokusai-ml-platform[ml]"

Configure API Key

export HOKUSAI_API_KEY="your-hokusai-api-key-here"

Register your model

hokusai model register \
  --token-id HLITI \
  --model-path ./models/final_model.pkl \
  --metric Accuracy \
  --baseline 0.80
Python SDK option also available for MLflow users. See complete guide for details.
2

Trigger Evaluation

Once your model is registered, trigger an evaluation run against the benchmark dataset. The system will automatically measure your model's performance against the target criteria.

Evaluation process:

  • Your model runs on the specified evaluation dataset
  • Performance is measured using Accuracy
  • Results are verified and recorded on-chain
  • Token rewards are calculated based on performance improvement
3

Claim Your Rewards

If your model meets or exceeds the benchmark target, you'll earn token rewards. Tokens are automatically minted and can be claimed from your dashboard.

What to Expect

Evaluation Timeline

Evaluation runs typically complete within 5-30 minutes, depending on model complexity and dataset size. You'll receive notifications when evaluation completes.

Performance Verification

All evaluation results are verified and recorded on-chain to ensure transparency and prevent manipulation. Your model's performance will be publicly visible.

Token Distribution

Tokens are minted automatically when your model achieves performance improvements. The amount is calculated based on the delta between your model's performance and the baseline, multiplied by the tokens-per-delta-one rate.

Support & Documentation

Need help? Visit our model submission guide for detailed instructions, code examples, and troubleshooting tips.