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NLPintermediate

End-to-End NLP: Text Summarization with Hugging Face Transformers

Develop a complete text summarization system from scratch, focusing on summarizing complex dialogues using the SAMSum dataset. This project emphasizes professional NLP pipelines, fine-tuning state-of-the-art models like Google Pegasus, and implementing modular Python code for maintainability and scalability.

10 lectures

What You Will Learn

Mastering the Hugging Face ecosystem for model and dataset integration
Implementing fine-tuning techniques for Transformer models on custom datasets
Building end-to-end ML pipelines for data ingestion, transformation, and model management
Developing modular and scalable Python scripts adhering to industry standards
Gaining hands-on experience with the transformers and PyTorch libraries
Understanding the nuances of real-world dialogue data using the SAMSum dataset
Evaluating and comparing different summarization models programmatically.

System Architecture

End-to-End NLP: Text Summarization with Hugging Face Transformers Architecture Diagram

High-level architecture overview of the End-to-End NLP: Text Summarization with Hugging Face Transformers .

What You'll Build

  • A fine-tuned Google Pegasus model for dialogue summarization
  • End-to-end NLP pipeline for data ingestion, transformation, training, and evaluation
  • Modular Python scripts for each stage of the pipeline
  • Prediction pipeline for real-time summarization via API
End-to-End NLP: Text Summarization with Hugging Face Transformers
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