Transformer-based NLP work covering pretrained models, text classification and, where verified, sentence-level semantic representations.
The problem
Modern NLP systems need a clear separation between the model family, the downstream task, the representation method and the evaluation evidence.
What was built
A notebook-based exploration of pretrained transformer workflows, transfer learning or fine-tuning, text classification and sentence-level representations, where confirmed by the final notebook review.
Technical approach
- Prepare text for a pretrained transformer workflow
- Adapt the representations to a downstream task
- Generate sentence-level semantic features where implemented
- Evaluate classification or semantic behaviour using evidence from the notebook
Engineering decisions
- Treat the model, dataset and metrics as unverified until the final notebook has been reviewed.
- Do not turn general familiarity with transformers into unsupported production claims.
- Use this project to show the method, and leave the evidence gaps visible.
How it was checked
- The repository area for Exercise-2 identifies transformer-based NLP work.
- The exact model, dataset, task, metrics and embedding approach are not yet confirmed from the repository.
What it shows
Hands-on work with modern language-model workflows, kept within disciplined limits on evaluation and disclosure.
About the source
Academic and R&D project. Some wording is deliberately left broad until the notebooks have been verified.