Datasheet parts: run the model evaluation
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 38/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- python
- Domain
- testing-qa
Research direction
Start with circuit_mcp/specscore.py to understand the scorer, then find the read_datasheet extraction entry point. Select about 10 datasheets across SOIC, QFN, connectors, and THT packages, create and human-check golden part specs, run the extraction, and record its scores. Done means the checked dataset and evaluation scores are recorded.
Written by the indexing model from the issue text.
Description
The scorer (circuit_mcp/specscore.py) exists, but the evaluation has never run: it needs a set of datasheets with golden part specs that a human has checked.
- Pick ~10 datasheets across packages (SOIC, QFN, connectors, THT).
- Write and check their golden specs.
- Run the model's
read_datasheetextraction against them and record the scores.
- Dominant language
- Python
- Stars
- 1
- Forks
- 1
- Avg merge
- 1h 36m
- Merged PRs (30d)
- 2
Getting set up
- No Dockerfile or Docker Compose file
- No pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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