Spent 3 days cleaning messy scraped product data until I discovered Latenode’s AI model marketplace. Their text classification models automatically categorize entries while NLP transforms raw text into structured JSON. Pro tip: Claude-2.1 works best for multi-language data normalization. How are others handling unstructured data at scale? Any favorite models for auto-detecting duplicate entries?
We process 50K listings/day using Latenode’s GPT-4 Turbo for entity recognition and Mistral for deduplication. Template available in marketplace - just feed raw data and get clean CSVs.
Key lesson: Always chain multiple models. Use cheaper model for initial cleanup, then specialized model for final validation. Saved 40% on processing costs while maintaining accuracy. For duplicates, combination of fuzzy matching and semantic analysis works better than either approach alone.
try the claude-2.1 + gpt-4o combo. claude does heavy lifting, gpt fixes edge cases. 70% cost save vs pure gpt-4
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