Types of Training Tasks

AI Training supports many different tasks. Each task is optimized for specific types of problems you want to solve.

Text Tasks

Text Classification

What it does: Sorts text into categories you define. Real-world examples:

Email spam detection
Customer feedback sentiment (happy/unhappy)
Support ticket routing
Content moderation
Language detection

What you need:

Text samples
Category labels for each sample
At least 50 examples per category (more is better)

Example data format:

text,label
"Great product, highly recommend!",positive
"Terrible experience, waste of money",negative
"The item arrived damaged",negative
"Excellent customer service",positive

Token Classification (NER)

What it does: Labels specific words or phrases in text. Real-world examples:

Extract names, dates, locations from documents
Identify product mentions in reviews
Find medical terms in clinical notes
Highlight important contract clauses
Tag parts of speech

What you need:

Text with marked entities
BIO format (Beginning, Inside, Outside) labels
Hundreds of annotated examples

Example data format:

John B-PERSON
Smith I-PERSON
visited O
New B-LOCATION
York I-LOCATION
yesterday B-DATE

Sequence to Sequence

What it does: Transforms one text into another. Real-world examples:

Language translation
Text summarization
Question answering
Text correction
Paraphrasing

What you need:

Input text
Desired output text
Pairs of input-output examples

Example data format:

input_text,target_text
"The quick brown fox jumps over the lazy dog","Le renard brun rapide saute par-dessus le chien paresseux"
"Hello, how are you?","Bonjour, comment allez-vous?"

Text Generation (LLM Fine-tuning)

What it does: Teaches language models new knowledge or behaviors. Real-world examples:

Custom chatbots
Domain-specific assistants
Code generation
Creative writing
Technical documentation

What you need:

Conversation examples or instruction-response pairs
Optional: Preference data for RLHF
Can work with as few as 100 examples

Example conversation format:

{
  "messages": [
    {"role": "user", "content": "What is photosynthesis?"},
    {"role": "assistant", "content": "Photosynthesis is the process plants use to convert sunlight into energy..."}
  ]
}

Image Tasks

Image Classification

What it does: Identifies what’s in an image. Real-world examples:

Product quality inspection
Medical image diagnosis
Wildlife identification
Document type classification
Facial expression recognition

What you need:

Images (JPG, PNG)
Category label for each image
At least 100 images per category

Folder structure:

data/
  cats/
    cat1.jpg
    cat2.jpg
  dogs/
    dog1.jpg
    dog2.jpg

Object Detection

What it does: Finds and locates multiple objects in images. Real-world examples:

Inventory counting
Security monitoring
Autonomous driving
Defect detection
People counting

What you need:

Images with bounding boxes
Labels for each box
COCO or YOLO format annotations

Structured Data Tasks

Tabular Classification

What it does: Predicts categories from spreadsheet-like data. Real-world examples:

Customer churn prediction
Fraud detection
Disease diagnosis
Credit approval
Equipment failure prediction

What you need:

CSV with features and labels
Numerical and categorical columns
Clean, preprocessed data

Example data:

age,income,credit_score,approved
25,45000,720,yes
35,65000,650,no
42,85000,780,yes

Tabular Regression

What it does: Predicts continuous values from structured data. Real-world examples:

House price prediction
Sales forecasting
Energy consumption estimation
Stock price prediction
Delivery time estimation

What you need:

CSV with features and target values
Numerical target column
Historical data

Advanced Training Methods

Supervised Fine-Tuning (SFT)

Standard training with examples and correct answers. Use when: You have good quality labeled data.

DPO (Direct Preference Optimization)

Train models using preference comparisons. Use when: You have examples of good vs bad outputs. Data format:

{
  "prompt": "Write a greeting",
  "chosen": "Hello! How can I help you today?",
  "rejected": "sup"
}

ORPO (Odds Ratio Preference Optimization)

Similar to DPO but more stable training. Use when: DPO training is unstable or overfitting.

Reward Modeling

Train a model to score outputs. Use when: Building a reward model for RLHF.

PPO (Proximal Policy Optimization)

Reinforcement learning from feedback. Use when: You have a reward model and want to optimize against it.

Task Selection Guide

Based on Your Data

If you have…	Choose this task
Text + categories	Text Classification
Text with entity labels	Token Classification
Input/output text pairs	Sequence to Sequence
Conversations	LLM Fine-tuning
Images + labels	Image Classification
Spreadsheet data	Tabular Classification/Regression

Based on Your Goal

If you want to…	Choose this task
Sort things into buckets	Classification
Extract information	Token Classification
Transform text	Sequence to Sequence
Create a chatbot	LLM Fine-tuning
Predict numbers	Regression
Find objects	Object Detection

Based on Difficulty

Easiest to start:

Text Classification
Image Classification
Tabular Classification

Medium difficulty:

Token Classification
Sequence to Sequence
LLM Fine-tuning (SFT)

Advanced:

DPO/ORPO training
Object Detection
PPO/RLHF

Data Requirements

Minimum Data Needed

Task	Absolute Minimum	Good Starting Point	Production Quality
Text Classification	50 per class	500 per class	5,000+ per class
Token Classification	100 documents	1,000 documents	10,000+ documents
Seq2Seq	100 pairs	1,000 pairs	10,000+ pairs
LLM Fine-tuning	50 examples	500 examples	5,000+ examples
Image Classification	100 per class	1,000 per class	10,000+ per class
Tabular	500 rows	5,000 rows	50,000+ rows

Data Quality Matters

Better to have 100 high-quality examples than 1,000 poor ones:

Accurate labels
Consistent formatting
Representative of real-world use
Balanced across categories

Multi-Task Training

You can train models for multiple tasks simultaneously:

Benefits

Share knowledge between tasks
More efficient use of data
Single model deployment

Example

Train one model to:

Classify sentiment
Extract entities
Summarize text

All with the same base model.

Task-Specific Settings

Text Tasks

Max sequence length: How much text to process
Tokenizer: How to split text into tokens
Special tokens: Task-specific markers

Image Tasks

Image size: Resolution to use
Augmentation: Rotation, flip, crop
Normalization: Pixel value scaling

Tabular Tasks

Feature engineering: Creating new columns
Scaling: Normalizing numeric values
Encoding: Handling categorical variables

Evaluation Metrics

Different tasks use different metrics:

Task	Common Metrics
Classification	Accuracy, F1, Precision, Recall
Token Classification	Entity-level F1, Token accuracy
Seq2Seq	BLEU, ROUGE, BERTScore
Generation	Perplexity, Human evaluation
Regression	MSE, MAE, R²
Object Detection	mAP, IoU

Combining Tasks

Pipeline Approach

Chain tasks together:

Classification → Route to specialized model
NER → Extract entities → Generate response
Translate → Summarize → Classify sentiment

Combine different data types:

Image + Text → Visual QA
Audio + Text → Speech recognition
Video + Text → Video understanding

Next Steps

Ready to dive deeper?

Model Types

Choose the right model architecture

Quick Start

Train your first model

​Types of Training Tasks

​Text Tasks

​Text Classification

​Token Classification (NER)

​Sequence to Sequence

​Text Generation (LLM Fine-tuning)

​Image Tasks

​Image Classification

​Object Detection

​Structured Data Tasks

​Tabular Classification

​Tabular Regression

​Advanced Training Methods

​Supervised Fine-Tuning (SFT)

​DPO (Direct Preference Optimization)

​ORPO (Odds Ratio Preference Optimization)

​Reward Modeling

​PPO (Proximal Policy Optimization)

​Task Selection Guide

​Based on Your Data

​Based on Your Goal

​Based on Difficulty

​Data Requirements

​Minimum Data Needed

​Data Quality Matters

​Multi-Task Training

​Benefits

​Example

​Task-Specific Settings

​Text Tasks

​Image Tasks

​Tabular Tasks

​Evaluation Metrics

​Combining Tasks

​Pipeline Approach

​Multi-Modal Tasks

​Next Steps

Model Types

Quick Start

Types of Training Tasks

Text Tasks

Text Classification

Token Classification (NER)

Sequence to Sequence

Text Generation (LLM Fine-tuning)

Image Tasks

Image Classification

Object Detection

Structured Data Tasks

Tabular Classification

Tabular Regression

Advanced Training Methods

Supervised Fine-Tuning (SFT)

DPO (Direct Preference Optimization)

ORPO (Odds Ratio Preference Optimization)

Reward Modeling

PPO (Proximal Policy Optimization)

Task Selection Guide

Based on Your Data

Based on Your Goal

Based on Difficulty

Data Requirements

Minimum Data Needed

Data Quality Matters

Multi-Task Training

Benefits

Example

Task-Specific Settings

Text Tasks

Image Tasks

Tabular Tasks

Evaluation Metrics

Combining Tasks

Pipeline Approach

Multi-Modal Tasks

Next Steps