📊 Matching (Mae)
Overview
Working implementation of Mae for Matching using a large configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted.
Quick check
Run python train.py --help for the built-in smoke test. Because this is a custom implementation, generic automatic loading APIs require an explicit adapter before use.
Default experiment recipe
The included configuration uses adam with a exponential schedule. These are starting values in the script, not evidence of a completed run. For a meaningful evaluation, train all baselines with the same data exposure, tuning budget, and random seeds.
Roadmap
Next steps, in no particular order: a cleaner entry point, input-shape docs, and a small evaluation script if time permits.
Evaluation guidance
A first evaluation could use a paired validation set. Report the metric across at least three seeds and include a matched-capacity baseline; keep logs and library versions with any published number.
Limitations
- weights are random-init, so outputs carry no signal
- robustness, fairness and domain transfer are untested
- the API may change without notice while the design settles
Repository status
- The Python file contains the model and runnable example or training entry point.
config.jsonrecords the generated architecture settings.training_args.jsonrecords the default experiment recipe.model.safetensorsis a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint.- No benchmark score is claimed in this repository.
Environment
| Item | Value |
|---|---|
| Python | >= 3.9 |
| torch | 2.x |
| safetensors | any recent |
Files
train.py— primary artifactREADME.md— this documentationconfig.json— architecture configurationtraining_args.json— default experiment settingsmodel.safetensors— initialization checkpoint
License
Distributed under the bsd-2-clause license. External datasets keep their own terms — check them separately.
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