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I will be your nlp expert
AI Engineer and Full Stack Developer: Expert in Scalable AI Solutions!
Experienced AI Engineer & Full Stack Developer with 7+ years of expertise in building intelligent, scalable, and efficient solutions. I specialize in AI-powered applications using GPT-4/3.5, OpenAI AP...
Sobre este Serviço
Custom NLP models that read your text like an expert fast, accurate, and private.
I'm Raihan, an AI/ML engineer & CTO at ClarioScope AI. I build production NLP models and they're live: my DeBERTa intent classifier hits 91.16% accuracy at ~22× the speed of a frontier API, alongside PHI detection (NER) and structured-extraction models.
What I build: Text classification intent, topic, spam, category, multi-label Named Entity Recognition (NER) names, IDs, dates, custom entities Sentiment & emotion analysis Information extraction free text structured JSON Fine-tuned transformers: BERT, DeBERTa, RoBERTa, ModernBERT
Why a custom model, not just ChatGPT? For classification & extraction, a fine-tuned model is far cheaper, faster, and more private than calling a big API on every request and it runs on your own infrastructure. Portfolio: raihan-js.github.io
You get: trained model + evaluation report (accuracy/F1) + inference code. Want it deployed as an API? I do that too.
Your data stays private. Need a generative LLM instead? See my fine-tuning gig.
Message me your task & data for an accurate quote!
Clientes com quem já trabalhei
GNatural Products
All Natural Skincare
I designed and developed Full WordPress Website for this client.
out. de 2020
Meu portfólio
Outros serviços de Ciência de dados e ML que eu ofereço
Perguntas frequentes
What NLP tasks can you handle?
Text classification (intent, topic, multi-label, spam), Named Entity Recognition (NER), sentiment/emotion analysis, and information extraction (free text → structured fields). If you're unsure which fits, message me and I'll recommend.
Which models do you use?
Transformer encoders: BERT, DeBERTa-v3, RoBERTa, ModernBERT, and lighter ones like DistilBERT when speed/size matter. I pick the model based on your accuracy, latency, and cost needs.
How accurate will the model be?
It depends on your data and task, and I'll report honest metrics (accuracy, precision/recall, F1) benchmarked against a baseline. For reference, my production intent classifier reaches 91.16%.
Why not just use ChatGPT or an LLM API?
For high-volume classification and extraction, a fine-tuned small model is dramatically cheaper, faster, and more private than calling a large API on every request — and it runs on your own infrastructure. I'll tell you honestly when an API is the better choice.
