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s6e8-lookup-transformer

My Lookup-Transformer solution for Kaggle S6E8: public LB ROC AUC 0.97041. Adopted in a 14th-place solution; 11 verified Kaggle references.

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This is 1 of 72 launches in hardware engineering and industrial utilities — see how it stacks up on momentum and crowding →

3 other launches read as similar to this one →

Details

External ID
1410128708
Source
GITHUB
Company
—
Product
s6e8-lookup-transformer
Website domain
kaggle.com
Launched
Oct. 8, 2026
Cohort
—
Upvotes
11
Upvotes percentile
0.16
Tags
classification, feature-engineering, kaggle, machine-learning, pytorch, tabular-deep-learning, transformer

Enrichment

Niche
hardware engineering and industrial utilities
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
transformer model for kaggle tabular competitions
Manually corrected
False

Could you build this?

Partial While the code for a PyTorch/scikit-learn Kaggle notebook can be prompted, designing a novel, competitive Lookup-Transformer architecture achieving top leaderboard ROC AUC requires competitive ML domain expertise.

What it would actually take: Stack uses PyTorch or JAX with tabular embedding architectures (Lookup-Transformer) and ensembling libraries (LightGBM/XGBoost/CatBoost). The hard part is feature engineering, developing custom categorical embedding lookup mechanisms that capture subtle interactions, and precise cross-validation schemes to prevent data leakage and hit 0.97+ ROC AUC. Requires strong competitive machine learning and tabular deep learning domain knowledge.

Competitors

Other products that read as similar to this one — 3 launches clear the similarity bar.

Attention rank: #3 of 4 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 152 days after the earliest competitor.

Other launches for this product