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NLP Pipeline Architect

Design and optimize production-grade NLP pipelines covering text preprocessing, tokenization, NER, sentiment analysis, and transformer-based architectures using spaCy and HuggingFace.

Gold
v1.0.016 activationsAI & Machine LearningTechnologyexpert

SupaScore

84.4
Research Quality (15%)
8.5
Prompt Engineering (25%)
8.5
Practical Utility (15%)
8.5
Completeness (10%)
8.5
User Satisfaction (20%)
8.2
Decision Usefulness (15%)
8.5

Best for

  • Design multilingual sentiment analysis pipeline for customer feedback processing
  • Build production NER system for extracting entities from legal documents
  • Optimize BERT-based text classification pipeline for latency-critical applications
  • Architect transformer-based document processing workflow with spaCy integration
  • Design domain adaptation strategy for medical text processing using HuggingFace models

What you'll get

  • Step-by-step pipeline architecture with specific spaCy components, tokenization strategy, and HuggingFace model selection with performance benchmarks
  • Production deployment blueprint including model distillation recommendations, batching strategies, and caching layers with latency estimates
  • Comprehensive training strategy with data requirements, evaluation metrics, and domain adaptation approach using transfer learning principles
Not designed for ↓
  • ×Training large language models from scratch or LLM fine-tuning strategies
  • ×Computer vision or multimodal AI pipeline design
  • ×Real-time speech processing or audio transcription pipelines
  • ×Generative text applications like chatbots or content creation
Expects

Clear specification of NLP task requirements including input text characteristics, target languages, output format, and production constraints like latency and throughput.

Returns

Detailed pipeline architecture with specific preprocessing steps, model recommendations, training strategies, and production deployment considerations with performance trade-offs.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

nlptext-preprocessingtokenizationnamed-entity-recognitionsentiment-analysistransformersspacyhuggingfacetext-classificationmultilingual-nlpdomain-adaptationbertpipeline-designtransfer-learning

Research Foundation: 8 sources (2 official docs, 3 paper, 2 books, 1 industry frameworks)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Prerequisites

Use these skills first for best results.

Works well with

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Common Workflows

Production NLP System Development

End-to-end workflow from NLP pipeline design through production deployment and monitoring

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