Terminal-Based NLP Research Toolkit from Tokens to Transformers

Added Jun 2025 3 design docs

NLP researchers and advanced students constantly re-implement the same building blocks, cleaning text, vectorizing it, training baselines, before they can test an actual idea, and notebook-centric tools fit poorly into scripted, repeatable research workflows. A well-designed command-line toolkit turns that recurring setup cost into single commands that compose into pipelines. The intern builds a comprehensive Python CLI covering the full breadth of practical NLP. Preprocessing commands handle cleaning, tokenization, stemming, and lemmatization with NLTK and spaCy; vectorization supports bag-of-words and TF-IDF through scikit-learn; and embedding commands generate Word2Vec and GloVe representations with t-SNE visualization for inspection. Classical task commands cover text classification, duplicate detection, extractive summarization, LDA topic modeling, and named entity recognition, while deep learning commands train LSTM and BERT models with PyTorch and TensorFlow for classification and NER. Utilities manage model saving and loading, NumPy and pandas underpin data handling, Jupyter supports exploration, and a FastAPI deployment command exposes trained models for batch prediction. The intern graduates with rare end-to-end range, from regex cleanup to transformer fine-tuning, plus solid CLI architecture skills and a genuinely reusable open-source research tool.

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