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BERT Fine-Tuning Command-Line Tool for Text Classification

Added Jun 2025 3 design docs

Transformer models dominate modern text classification, but the distance between downloading a pre-trained BERT checkpoint and having a reliable classifier trained on your own labeled data trips up many practitioners. Teams in finance, healthcare, and education all share the same need: fine-tune on a private dataset, evaluate honestly, and run predictions repeatably, ideally from scripts rather than notebooks.

The intern builds a Python command-line application that packages this workflow end to end using the Transformers library. Commands accept a user-provided labeled dataset, run preprocessing and subword tokenization, fine-tune a BERT-based model on the classification task, and report evaluation metrics computed with scikit-learn such as accuracy and per-class F1. Further commands save and reload fine-tuned models and run inference over new text files, writing predictions to structured output so the tool slots cleanly into larger pipelines. Sensible defaults with overridable training options make the tool approachable without hiding what matters.

The project teaches the intern how transfer learning works in NLP practice, tokenization, fine-tuning dynamics, and evaluation discipline, and demonstrates the ability to wrap state-of-the-art models in a dependable command-line interface that other people can actually use.

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