deit-contrastive-notebook

DeiT for Contrastive

Overview

A small DeiT implementation for Contrastive, packaged with an explicit configuration and an initialization checkpoint. The huge variant is a reproducible starting point, not a trained model release.

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.

Architecture

Default experiment recipe

The included configuration uses rmsprop 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.

Quick check

python predict.py --help

Inspect the script's __main__ block for its generated smoke-test example. Because this is a custom implementation, generic automatic loading APIs require an explicit adapter before use.

Evaluation guidance

A useful first evaluation would use a task-specific held-out set, report the task metric across at least three seeds, and include a matched-capacity baseline. Keep training logs and environment versions with any published result.

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.

Files

• predict.py — primary artifact
• README.md — this documentation
• config.json — architecture configuration
• training_args.json — default experiment settings
• model.safetensors — initialization checkpoint

License

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