Phoenix - Your AI AssistantNLP Text Classification – BERT Fine-Tuning & API
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5 days
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What you get with this Offer
I will build an end-to-end NLP text classification pipeline — fine-tuning a BERT, RoBERTa, or DistilBERT model on your labelled text dataset, evaluating with precision, recall, and F1 per class, and deploying a FastAPI inference endpoint containerised with Docker for production use. Text classification using transformer fine-tuning consistently outperforms traditional ML on short to medium text when sufficient labelled examples are available; the challenge is implementing the fine-tuning correctly with proper tokenisation, training parameter selection, and evaluation methodology.
The pipeline covers data preparation and tokenisation, model selection and fine-tuning with Hugging Face Trainer or custom training loop, evaluation on held-out test set with per-class metrics and confusion matrix, error analysis on misclassified examples, FastAPI inference endpoint with batch and single prediction support, Dockerfile for containerised deployment, and inference latency benchmarking.
This service suits data science teams and businesses building ticket categorisation, content moderation, sentiment analysis, intent detection, or document routing systems who need a transformer-based classifier delivering production-grade accuracy.
The pipeline covers data preparation and tokenisation, model selection and fine-tuning with Hugging Face Trainer or custom training loop, evaluation on held-out test set with per-class metrics and confusion matrix, error analysis on misclassified examples, FastAPI inference endpoint with batch and single prediction support, Dockerfile for containerised deployment, and inference latency benchmarking.
This service suits data science teams and businesses building ticket categorisation, content moderation, sentiment analysis, intent detection, or document routing systems who need a transformer-based classifier delivering production-grade accuracy.
What the Freelancer needs to start the work
Please share your labelled text dataset (CSV with text and label columns), your label set and class definitions, minimum acceptable per-class F1 score, your deployment environment, and any latency requirements for the inference endpoint.