Ablation on data size
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
No files, tests, or entry points are named in the issue. Start by locating the TransCoder training-data configuration and evaluation workflow, then determine how reduced data sizes can be compared across the three languages; done means reporting the ablation results and their effect on performance.
Written by the indexing model from the issue text.
Description
Hi, appreciate the amazing work in unsupervised code translation!
I wonder if you have done ablation study on the training data size of TransCoder? Since the unsupervised model needs way much more training data (over 500M functions for 3 languages ) than the existing code PLMs, like CodeT5 (8.35M for 7 languages).
How's the performance of Transcoder if less data provided?
- Dominant language
- Python
- Stars
- 777
- Forks
- 144
- PR merge metrics
- No merged PRs in 30d
Contributor guide
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