Inference code for "TabDPT: Scaling Tabular Foundation Models on Real Data"
C-Rank Grade: B (Solid) - 51/100
External PR Merge Rate: 50%
Response Time: <1h
First Timer Success: 100%
layer6ai-labs/TabDPT-inference has a recorded first-timer success rate of 100.0%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in layer6ai-labs/TabDPT-inference respond to incoming external pull requests in approximately 0.1 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 51.0 places layer6ai-labs/TabDPT-inference in the B tier.
The external contributor pull request merge rate for layer6ai-labs/TabDPT-inference is 50.0%, based on public PR activity from non-core contributors.
layer6ai-labs/TabDPT-inference does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Collaborative maintainer environment in layer6ai-labs/TabDPT-inference. Reviews community pull requests with focus on project quality.
Discussions in layer6ai-labs/TabDPT-inference focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/layer6ai-labs/TabDPT-inference?utm_source=github&utm_medium=badge)
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 14:00 UTC (14:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 14:00 to 18:00 UTC (14:00 - 18:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (62% approval). Large PRs (>500 lines) take ~1 hours (22% approval). Keep your diff compact for rapid review.
Enter your branch size and test coverage below to forecast review turnaround and get 1-click git splitting commands.
No cached good first issues currently tracked for layer6ai-labs/TabDPT-inference.
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When evaluating whether to contribute to layer6ai-labs/TabDPT-inference, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of layer6ai-labs/TabDPT-inference acknowledge new external contributions in approximately <1 hour. Out of all submitted pull requests from non-core authors in the last 180-day window, 50.0% were successfully merged into the primary branch.
layer6ai-labs/TabDPT-inference has a recorded first-timer success rate of 100.0%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~<1 hour. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rankâ„¢ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 51.0 places layer6ai-labs/TabDPT-inference in the Solid tier.
The external pull request merge rate is 50.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
layer6ai-labs/TabDPT-inference does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for layer6ai-labs/TabDPT-inference are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.