Repair the mojibake in multilingual email
Your goal
Find the planted defect in dirty/emails.jsonl and flag every affected line in findings.jsonl.
- 1 defect
- 41 KB of data
- FromBeyond English: language, script and region
- Halden Freight, all sites
Pro lab
This lab is in Pro.
The brief, the hints, and grading against the kit’s scorer are in Pro. Stage 1’s lab is free.
- Every stage and specialisation, in full
- Every lab and every role project
- Halden weeks 2–10, on your record
- Every drill, live in the playground
- All solutions, plus the server judge
- Lab Assistant and every Teach with AI
Defects to find
D10Mojibake in names and text
14 lines to find
How it’s graded
Precision ≥ 0.95 and recall ≥ 0.95, for each defect.
The bar: the bar the Kelverne capstone sets for every defect.
Halden · Your client in the residency: Halden Freight, a simulated freight company you work with for ten weeks. Its labs run on the same company's data. Your client: Halden
Here the client data (the estate: a fictional client’s files, with documented problems planted in them) is already generated, with the kit’s seed: your code reads the files under dirty/. Run tests and Submit both grade your findings with the kit’s score.py, scoped as the task’s command scopes it; only Submit goes on your record. The written parts of the task aren’t graded here; they’re for your notes or your reviewer.
Run tests calls find() and grades what it returns. Submit does the same and saves the result.
⌘/Ctrl+Enter runs the tests, add Shift to submit. First run downloads 41 KB of data.