Ml Nlp Transformers

Professional workflows for modern Natural Language Processing using Transformer architectures.

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ML NLP Transformers

Transformers changed everything. This skill focuses on the Hugging Face ecosystem.

The Pipeline

  1. Tokenization: Converting text to numbers.
  2. Model Loading: Using AutoModelForSequenceClassification or similar.
  3. Training / Fine-Tuning: Using the Trainer API for simplicity.

Core Tasks

  • Sentiment Analysis: Understanding emotion.
  • Named Entity Recognition (NER): Extracting entities (People, Places).
  • Summarization: Condensing text.
  • Embeddings: Creating vector representations for semantic search.

Best Practices

  • Parameter-Efficient Fine-Tuning (PEFT): Using LoRA to adapt large models with minimal resources.
  • Quantization: Compressing models for faster inference.

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