Kaggle Competition Submission
🏅 Ranked #44 on the Public Leaderboard
This repository contains my solution for the Chatbot Arena Preference Prediction Kaggle competition.
The challenge was to predict which chatbot response a human judge preferred, given a prompt and two responses.
The submission ranked 44th on the public leaderboard out of hundreds of global teams 🌍.
📂 notebooks/ # Exploratory data analysis and experiments
📄 README.md # Project overview (this file)- ✨ Fine-tuned transformer-based models on human preference data.
- 🔍 Analyzed semantic similarities between responses.
- ⚖️ Normalized token lengths and prompt alignment.
- 🔀 Applied ensemble strategies to combine model strengths.
- 🧪 Used stratified validation to handle subjectivity and avoid leakage.
| Tool | Purpose |
|---|---|
| 🐍 Python | Programming Language |
| 🤗 Transformers | Pretrained NLP Models |
| 🔥 PyTorch | Deep Learning Framework |
| 📊 Scikit-learn | Metrics & Utilities |
| 📚 Pandas | Data Manipulation |
| 📈 Matplotlib | Visualization |
| Metric | Value |
|---|---|
| Leaderboard Rank | 🥇 #44 |
| Final Score | [Insert final score] |
| Total Teams | [Insert number] |
- Human preferences in NLP are highly nuanced and often subjective.
- Even small model tweaks (like input formatting or length balancing) had large effects on performance.
- Ensembling and careful validation strategy were critical to climb the leaderboard.
Huge thanks to:
- The Kaggle community for insightful discussions and open-source notebooks.
- Competition organizers for an exciting and innovative challenge.
- Open-source contributors to libraries like Hugging Face & PyTorch.
If you're interested in discussing the project or collaborating, please reach out at bhandeystruck@gmail.com