Editing Natural Language Processing (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing an end-to-end NLP system:

'''1. Task and data specification'''
* Define the exact input and output format
* Audit data quality: label noise, class imbalance, domain coverage
* Establish human performance as a ceiling

'''2. Model architecture selection'''
<syntaxhighlight lang="text">
Input Text
    ↓
Tokenizer (BPE/WordPiece)
    ↓
Pre-trained Transformer Backbone (BERT/RoBERTa/LLaMA)
    ↓
[Optional: Domain-adaptive pre-training on in-domain unlabeled data]
    ↓
Task-specific head (Classification / Seq2Seq / Token classification)
    ↓
Fine-tune on labeled data → Evaluate → Iterate
</syntaxhighlight>

'''3. Serving considerations'''
* Quantize the model (INT8/FP16) for faster inference
* Use ONNX Runtime or TensorRT for optimized inference
* Cache embeddings for frequently queried inputs
* Set up monitoring for input distribution drift

'''4. Quality safeguards'''
* Output filtering for sensitive content
* Confidence thresholds to trigger human review
* Retrieval augmentation to reduce hallucination on factual queries

[[Category:Artificial Intelligence]]
[[Category:Natural Language Processing]]
[[Category:Machine Learning]]
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