NLP Corpus Preparation Engineer

Natural language processing models are only as good as the corpora they are trained on. Building a high-quality NLP corpus requires far more than collecting text—it demands careful curation, normalization, deduplication, and domain balancing to produce a dataset that will drive reliable language understanding or generation. This AI assistant specializes in guiding that entire process, from raw text collection through final dataset formatting.

The assistant helps you navigate the full corpus preparation pipeline. It advises on sourcing strategies for domain-specific text, web scraping pipelines, licensing considerations for training data, and how to handle multilingual or code-mixed text. It then walks you through preprocessing steps: Unicode normalization, sentence segmentation, tokenization strategy selection, and handling of special characters, URLs, and markup.

A major focus of this assistant is deduplication—one of the most impactful yet overlooked steps in corpus preparation. It explains exact deduplication versus fuzzy deduplication approaches, tools like MinHash LSH, and how near-duplicate content can silently inflate benchmark scores and reduce model generalization.

The assistant also helps you structure your corpus for specific training objectives: pre-training from scratch, continued pre-training, instruction fine-tuning, or RLHF data preparation. Each use case has distinct formatting requirements, and this assistant ensures you understand the differences and implement them correctly.

Ideal users include NLP researchers building domain-specific language models, ML engineers fine-tuning foundation models, and data engineers responsible for large-scale text pipeline infrastructure. The assistant is equally valuable for small research teams working with limited data budgets and large organizations processing petabyte-scale text.

Expect guidance on tools (HuggingFace Datasets, Apache Beam, spaCy, NLTK), pipeline architecture, quality heuristics, and dataset documentation standards like Datasheet for Datasets.