End-to-End NLP Pipeline — Text Classification, NER or Sentiment

I will design and build a complete, production-ready NLP pipeline for your text classification, named entity recognition (NER), sentiment analysis, or intent detection use case — from raw text ingestion through preprocessing, transformer model fine-tuning (BERT, RoBERTa, DistilBERT, or domain-specific variant), evaluation, and a deployable inference API. NLP is one of the highest-value AI applications for businesses processing customer feedback, support tickets, contracts, emails, or social media — but building a reliable pipeline requires expertise across linguistics, deep learning, and software engineering simultaneously.

The pipeline covers text cleaning and tokenisation, model fine-tuning on your labelled dataset, evaluation with precision/recall/F1 per class, error analysis on misclassified samples, and a FastAPI or Flask inference endpoint ready for integration with your application. The pipeline is containerised with Docker for consistent deployment across environments.

This service suits businesses with a text processing need — automating support ticket categorisation, extracting entities from contracts, classifying customer sentiment at scale, or building intent recognition for a conversational interface — who need a custom model that outperforms generic off-the-shelf NLP APIs on their specific domain.