Ribeiro87/cnn-transformer-checkpoint

A compact Cnn Transformer for Classification

Overview

Working implementation of Cnn Transformer for Classification using a giant configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted.

Feedback on the structure is welcome; the code is intentionally short.

Default Experiment Recipe

The generated recipe uses adam with a polynomial schedule.

These values exist so the script runs end-to-end. A meaningful experiment needs its own tuning budget and at least three seeds.

Acknowledgments

Thanks to the maintainers of the open-source libraries this project leans on.

Evaluation Guidance

A first evaluation could use a task-specific labeled split. Report the metric across at least three seeds and include a matched-capacity baseline; keep logs and library versions with any published number.

Limitations

The initialization checkpoint has not been trained or audited for robustness, fairness, or domain transfer. The implementation should be treated as an experimental starting point. Results from a future trained checkpoint must be documented separately from the defaults shipped here.

Repository Status

• The Python file contains the model and runnable example or training entry point.
• config.json records the generated architecture settings.
• training_args.json records the default experiment recipe.
• model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint.
• No benchmark score is claimed in this repository.

Quick Check

Run python predict.py --help for the built-in smoke test. Because this is a custom implementation, generic automatic loading APIs require an explicit adapter before use.

Files

File	Purpose
predict.py	primary artifact
README.md	this documentation
config.json	architecture configuration
training_args.json	default experiment settings
model.safetensors	initialization checkpoint
examples/sample_usage.py	usage sketch

License

Released under bsd-2-clause. Review the source-data terms separately when this repository is used with external datasets.