transformers-tutorials

Why we included this project

Learning to fine-tune a transformer by reading Hugging Face documentation is slow going, and this is the kind of repository that spares you the trial and error. It collects working Jupyter notebooks that show the full pattern for specific tasks: text classification with BERT or DistilBERT, named entity recognition, and sequence-to-sequence jobs like summarization with T5. Each notebook is self-contained and paired with experiment tracking notes, so a small team can follow a complete run from data preparation through training to evaluation before adapting the code to its own dataset. Treat it as a learning resource rather than a library, and pin the versions you use, since examples written against a fast-moving ecosystem date quickly.