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rush
arpit-bhayani/rush
rush is a machine learning model from arpit-bhayani. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This directory stores calibrated risk parameters and trained neural head checkpoints for Rush.
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Public
Updated Sep 21, 2026
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12.1 MB
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.pt12.1 MB · 100%
At a glance
- Access
- Public
- Created
- Sep 21, 2026
- Updated
- Sep 21, 2026
- SHA
- 7ac7f171
Model card
From the Hugging Face model README
Checkpoints
This directory stores calibrated risk parameters and trained neural head checkpoints for Rush.
Tracked Files
conformal_calibration.json: Calibrated non-conformity quantiles and risk thresholds evaluated on held-out test splits (e.g. Banking77).
Binary Weights (*.pt - Ignored from Git)
The following binary weights are kept locally or downloaded from Hugging Face Hub / GitHub Releases:
semantic_option_bank.pt: TrainedSemanticOptionBankmetric tensor ($W$) and temperature ($\tau$) on Banking77.span_pointer.pt: TrainedSpanPointerHeadstart/end boundary projection layers on SQuAD.
Reproducing Checkpoints Locally
To train or re-calibrate the checkpoints from scratch:
# 1. Train Semantic Option Bank on Banking77 and calibrate conformal quantiles
python3 rush/train/train_banking77.py
# 2. Train Extractive Span Pointer Head on SQuAD
python3 rush/train/train_span_pointer.py
- Created
- Sep 21, 2026
- Updated
- Sep 21, 2026