EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·25d ago·5 min read
I had Gemini train its own replacement for $9
TL;DR
A fine-tuned GLiNER model was developed to identify brands, models, and materials in Reddit comments about chef's knives, achieving an F1 score of 0.83. This approach significantly reduced costs compared to using Gemini for each comment.
✦ Why It Matters
Consider fine-tuning existing models to reduce costs and improve efficiency in your own projects.
Key Takeaways
- Use existing models to reduce costs in data labeling tasks.
- Focus on tensor configuration to avoid training pitfalls.
- Validate models with a locked dataset before training.
- Implement per-class thresholds to improve recall on specific categories.
How It Works
Gemini labeled Reddit comments for brands, models, and materials, which were then used to fine-tune GLiNER. The model was trained to recognize specific product names while avoiding generic terms, with careful attention to input tensor configurations to ensure effective learning.