training-patterns

# ML Training Patterns

**Purpose:** Provide production-ready training templates, configuration files, and automation scripts for common ML training scenarios including classification, generation, fine-tuning, and PEFT/LoRA approaches.

**Activation Triggers:**
- Building text classification models (sentiment, intent, NER, etc.)
- Training text generation models (summarization, Q&A, chatbots)
- Fine-tuning pre-trained models for specific tasks
- Implementing PEFT (Parameter-Efficient Fine-Tuning) with LoRA
- Setting up training pipelines with HuggingFace Transformers
- Configuring training hyperparameters and optimization
- Preparing datasets for model training

**Key Resources:**
- `scripts/setup-classification.sh` - Classification training setup automation
- `scripts/setup-generation.sh` - Generation training setup automation
- `scripts/setup-fine-tuning.sh` - Full fine-tuning setup automation
- `scripts/setup-peft.sh` - PEFT/LoRA training setup automation
- `templates/classification-config.yaml` - Classification training configuration
- `templates/generation-config.yaml` - Generation training configuration
- `templates/peft-config.json` - PEFT/LoRA configuration
- `examples/sentiment-classifier.md` - Complete sentiment classification example
- `examples/text-generator.md` - Complete text generation example

## Training Scenarios Overview

### 1. Text Classification

**Use cases:** Sentiment analysis, intent classification, topic categorization, spam detection, named entity recognition (NER)

**Key characteristics:**
- Input: Text → Output: Class label(s)
- Typically uses encoder models (BERT, RoBERTa, DistilBERT)
- Fast inference, suitable for production
- Requires labeled training data

**Setup command:**
```bash
./scripts/setup-classification.sh <project-name> <model-name> <num-classes>
```

**Example:**
```bash
./scripts/setup-classification.sh sentiment-model distilbert-base-uncased 3
```

### 2. Text Generation

**Use cases:** Summarization, question answeri