Checkpoints on code stylometry
Authors/Creators
- 1. Università di Bologna
- 2. Télécom Paris
Description
This release contains three trained models (checkpoints) related to the "Stylometry for Real-World Expert Coders: a Zero-shot Approach" paper, respectively:
- MLAllVocaBSoftAtt referees to the soft attention model trained with infoNCE loss without B.P.E, with bounding.
- SoftAtt64k104AuthClass referees to the soft-attention model trained in a classification setup with 64k B.P.E. token, with bounding.
- SelfAtt64k104authClass referees to the self-attention model
Files
Restricted
The record is publicly accessible, but files are restricted. <a href="https://zenodo.org/account/settings/login?next=https://zenodo.org/records/8160649">Log in</a> to check if you have access.
Request access
If you would like to request access to these files, please fill out the form below.
You need to satisfy these conditions in order for this request to be accepted:
Artifacts will be available after peer reviewing
You are currently not logged in. Do you have an account? Log in here