Gboard Prediction Accuracy Enhancement
Budget: ₹750 – ₹1,250 INR
I need a developer who can build a custom feature for Google’s Gboard that focuses squarely on improving the accuracy of its text-prediction engine. This isn’t a bug-fixing exercise or a simple integration; it is genuine feature development aimed at smarter, more context-aware suggestions while users type.
Your job will start with analysing the existing prediction logic, then designing and implementing modifications—whether that means plugging in a new on-device language model, refining the current n-gram pipeline, or introducing lightweight neural-network tweaks that can run efficiently in real time. Kotlin or Java proficiency for the Android input-method framework is essential, and a solid grasp of NLP techniques, TensorFlow Lite (or similar), data handling, and privacy-first personalisation approaches will make the work smoother.
Deliverables
• Updated Gboard module (source + compiled APK) with measurable accuracy gains against the current baseline.
• A repeatable test script or demo app that highlights side-by-side prediction results.
• Brief technical report explaining model changes, datasets used, and any runtime or battery-impact considerations.
I’ll consider the task complete once the new build consistently outperforms stock Gboard predictions on a representative corpus without noticeable latency. If this challenge excites you, let’s discuss your proposed approach and timeline.
Your job will start with analysing the existing prediction logic, then designing and implementing modifications—whether that means plugging in a new on-device language model, refining the current n-gram pipeline, or introducing lightweight neural-network tweaks that can run efficiently in real time. Kotlin or Java proficiency for the Android input-method framework is essential, and a solid grasp of NLP techniques, TensorFlow Lite (or similar), data handling, and privacy-first personalisation approaches will make the work smoother.
Deliverables
• Updated Gboard module (source + compiled APK) with measurable accuracy gains against the current baseline.
• A repeatable test script or demo app that highlights side-by-side prediction results.
• Brief technical report explaining model changes, datasets used, and any runtime or battery-impact considerations.
I’ll consider the task complete once the new build consistently outperforms stock Gboard predictions on a representative corpus without noticeable latency. If this challenge excites you, let’s discuss your proposed approach and timeline.