Skip to main content
Back to Prompt Library
implementation

Construct PyTorch Model for Chromatin Binding

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Pioneer Factor Cohesin Chromatin Binding Predictor with PyTorch

Format
Code-aware
Lines
1
Sections
1
Linked challenge
Pioneer Factor Cohesin Chromatin Binding Predictor with PyTorch

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines
1 sections
No variables
0 checklist items
Write a Python script using `torch` defining a `nn.Module` class that processes 1000bp one-hot encoded DNA sequences and outputs cohesin binding logits.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.