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Monitoring School Meal Quality - 1
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Module 5
Baseline 2: Text-Only Complaint Classification
1
Transformer Architecture and Self-Attention
Explain the core concepts of the Transformer architecture and self-attention mechanism.
2
Fine-tuning BERT for Specific Tasks
Describe how fine-tuning adapts a pretrained language model like BERT for a specific task.
3
BERT Tokenization with Hugging Face: Padding and Truncation
Tokenize complaint texts for BERT using a Hugging Face tokenizer, handling padding and truncation.
4
Fine-tuning BERT for Complaint Classification with Hugging Face Trainer
Fine-tune a pretrained BERT model for complaint classification using the high-level Hugging Face `Trainer` API.
5
Evaluating Text Classifier Performance with F1-Score and Per-Class Metrics
Evaluate the text classifier's performance using F1-score and per-class metrics.
Previous module
Baseline 1: Image-Only Meal Quality Classification
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Feature Enhancement with Object Detection