Multifunction NLP AI Development

This project centres on building an end-to-end Natural Language Processing system that handles three distinct tasks: text generation, sentiment analysis, and named entity recognition. The solution should be production-ready, modular, and easy to extend, making use of well-maintained Python libraries such as Hugging Face Transformers, PyTorch or TensorFlow, and standard preprocessing tools (spaCy, NLTK, or similar).

A typical workflow might involve fine-tuning large language models for generation, training lightweight classifiers for sentiment detection, and integrating an entity recognition component that supports custom taxonomies. Clean code, reproducible experiments, and clear documentation are essential so that future contributors can retrain or swap models as data evolves.

I am lookng for AI specialist partners who can assist me with european based projects who are maybe in asia or americas.

Deliverables
• Source code organised by task with concise README files
• Pre-trained model weights and scripts for inference and retraining
• Sample data pipeline scripts (tokenisation, augmentation, evaluation)
• A short report summarising model choices, hyper-parameters, and benchmark results on public or synthetic datasets

When you respond, focus on relevant experience—particularly projects where multiple NLP tasks were combined or where state-of-the-art language models were deployed in real applications. Links to repositories, live demos, or published papers that showcase similar work will help me gauge fit quickly.