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Large Language Models applied to finance

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replied to Sri-Vigneshwar-DJ's post about 8 hours ago
Do you think domain-specific embedding fine-tuners are needed? I've been working with embeddings for marketing use cases and noticed something: most embeddings don't get marketing concepts very well. They're trained in general-purpose ways. The Issue I'm Seeing When I search marketing content with general embeddings: "organic growth" returns farming articles "conversion funnel" matches industrial equipment "brand lift" doesn't connect to campaign effectiveness Marketing jargon like CAC, ROAS, CTR aren't properly understood My Question Do you think domain-specific embeddings are needed for marketing? Some thoughts: Marketing has its own vocabulary and concept relationships General models trained on Wikipedia/web crawl miss these nuances But is fine-tuning worth the effort vs just using more retrieval tricks? Quick Example I fine-tuned all-mpnet-base-v2 on ~1000 marketing concept pairs and saw 15-20% better retrieval accuracy. But I'm curious: Has anyone else tried this for marketing or other domains? When do you think domain-specific embeddings are actually necessary vs overkill? Are there better approaches I'm missing? https://huggingface.co/blog/Sri-Vigneshwar-DJ/why-your-marketing-rag-system-needs-domain-specifi
reacted to prithivMLmods's post with 🚀 about 14 hours ago
Have built the new Image Studio with the Gemini Image Gen models for the following multiple tasks: `imagen-4.0-fast-generate-001` model for Image Generation (Text-to-Image) and Multi-Image Editing (Image-to-Image), and Draw-to-Image powered by `gemini-2.5-flash-image` (aka Nano Banana). ⭐ Gemini-Image-Studio: https://huggingface.co/spaces/prithivMLmods/Gemini-Image-Studio (Latest) 🤞 Old-App: https://huggingface.co/spaces/prithivMLmods/Nano-Banana-AIO 🥊 GitHub: https://github.com/prithivsakthiur/gemini-image-studio-hf To proceed, you need to add your Gemini API key. Your API key is stored only for the duration of your session and will be lost when you reload or exit the page. It will not be shared or exposed anywhere.
replied to Sri-Vigneshwar-DJ's post about 14 hours ago
Do you think domain-specific embedding fine-tuners are needed? I've been working with embeddings for marketing use cases and noticed something: most embeddings don't get marketing concepts very well. They're trained in general-purpose ways. The Issue I'm Seeing When I search marketing content with general embeddings: "organic growth" returns farming articles "conversion funnel" matches industrial equipment "brand lift" doesn't connect to campaign effectiveness Marketing jargon like CAC, ROAS, CTR aren't properly understood My Question Do you think domain-specific embeddings are needed for marketing? Some thoughts: Marketing has its own vocabulary and concept relationships General models trained on Wikipedia/web crawl miss these nuances But is fine-tuning worth the effort vs just using more retrieval tricks? Quick Example I fine-tuned all-mpnet-base-v2 on ~1000 marketing concept pairs and saw 15-20% better retrieval accuracy. But I'm curious: Has anyone else tried this for marketing or other domains? When do you think domain-specific embeddings are actually necessary vs overkill? Are there better approaches I'm missing? https://huggingface.co/blog/Sri-Vigneshwar-DJ/why-your-marketing-rag-system-needs-domain-specifi
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