Hugging Face Transformers Model Development Training Course

Course overview

Pre-trained transformer models can shorten the path from an AI use case to a working capability, but teams often struggle to turn a promising notebook into a reliable, measurable solution. Selecting an unsuitable checkpoint, preparing inconsistent labels, using weak evaluation metrics, or fine-tuning without attention to compute cost can produce models that look convincing in demonstrations but fail under business conditions. This course equips practitioners to develop task-specific NLP and multimodal transformer models with the Hugging Face ecosystem, using disciplined data, training, evaluation and deployment practices.

Participants work directly with Hugging Face Transformers to load pipelines and checkpoints, inspect tokenizers and model configurations, prepare datasets, fine-tune models with the Trainer API, and evaluate results using task-appropriate metrics. The course covers text classification, token classification, question answering and text generation workflows, alongside parameter-efficient fine-tuning with PEFT concepts, experiment configuration, model documentation and inference optimisation. Participants learn how to diagnose overfitting, class imbalance, poor label quality and unsuitable metrics before those issues become expensive production problems.

Instruction combines focused technical demonstrations with guided coding labs in Python. Each participant builds a documented transformer-model development workflow around a realistic business dataset, progressing from data profiling and baseline inference to fine-tuning, evaluation and inference packaging. They leave with a reusable project structure containing preprocessing code, training configuration, metric reports, an evaluation checklist and a model card suitable for internal review. This course is particularly valuable for teams building customer-service classification, document extraction, search, knowledge-assistance, moderation or domain-specific language AI capabilities.

Course objectives

By the end of this course, participants will be able to:

• Select Hugging Face checkpoints by comparing model architecture, task head, licence, size and inference constraints
• Prepare versioned training and validation datasets with Hugging Face Datasets, tokenizers and reproducible preprocessing functions
• Build baseline inference workflows using the Transformers pipeline API and task-specific model classes
• Fine-tune sequence classification and token classification models with the Trainer API and TrainingArguments
• Configure tokenisation, padding, truncation and data collators for efficient batched model training
• Evaluate transformer models using precision, recall, F1, confusion matrices and error-slice analysis
• Apply parameter-efficient fine-tuning methods to adapt larger models within constrained GPU budgets
• Produce a model card, inference script and acceptance checklist for a reviewed transformer-model release

Benefits of attending

Target competencies

Who should attend

• Machine Learning Engineers — who need to fine-tune, evaluate and package transformer models for business applications
• Data Scientists — who need a repeatable route from labelled text data to validated NLP model performance
• AI Engineers — who build retrieval, classification, extraction or generative AI components using open-source models
• Python Developers — who need to integrate Hugging Face models into internal services and product features
• Data and Analytics Leads — who must assess the feasibility, cost and evaluation evidence for transformer initiatives
• MLOps Engineers — who support reproducible training runs, model artefacts and controlled handover to deployment teams

Requirements and prerequisites

Participants should be comfortable writing and running Python code, including functions, dictionaries, lists and package imports. They should understand the purpose of training, validation and test datasets, and have basic familiarity with supervised machine learning concepts such as features, labels, overfitting and evaluation metrics. Experience with PyTorch, Jupyter notebooks or command-line Git is helpful but not essential; required commands and APIs are introduced during the course. Participants do not need prior Hugging Face experience, advanced deep-learning mathematics, transformer architecture implementation experience or prior GPU administration knowledge.

Training methodology

The five days alternate instructor-led technical walkthroughs with guided Python labs using realistic labelled-text scenarios. Participants inspect datasets, run baseline pipelines, fine-tune models through the Hugging Face Trainer API, compare evaluation outputs and investigate failure cases such as ambiguous labels and class imbalance. Short case discussions address model choice, licensing, compute limits and release evidence. Working pairs review each other’s metric reports and model cards. On the final day, participants complete an application plan that maps their own use case to data requirements, model options, evaluation criteria and operational handover steps.

Course outline

Day 1: Hugging Face foundations and model selection

• Transformer architecture concepts: encoders, decoders and task heads
• Hugging Face Hub model repositories, revisions and model metadata
• Pipeline API for rapid baseline inference
• AutoTokenizer, AutoModel and task-specific AutoModel classes
• Checkpoint selection by task, parameter count, language coverage and licence
• Tokenizer vocabulary, subword tokenisation and special tokens
• Model configuration, maximum sequence length and inference constraints

Workshop: Participants evaluate several Hub checkpoints against a business text-classification scenario and produce a justified model-selection recommendation.

Day 2: Datasets and preprocessing for transformer training

• DatasetDict structure and split management with Hugging Face Datasets
• Loading CSV, JSON and Parquet data into Arrow-backed datasets
• Label schema design for classification and token classification tasks
• Batched mapping functions for reproducible tokenisation
• Truncation, padding and attention-mask configuration
• Dynamic padding with DataCollatorWithPadding
• Data quality checks for duplicates, leakage, imbalance and annotation inconsistency

Workshop: Participants transform a raw labelled dataset into tokenised train, validation and test splits and deliver a data-quality findings log.

Day 3: Fine-tuning task-specific transformer models

• Sequence classification fine-tuning with AutoModelForSequenceClassification
• TrainingArguments for epochs, batch size, learning rate and checkpointing
• Trainer API training loops and callback behaviour
• Validation strategy, early stopping and best-model selection
• GPU memory management through gradient accumulation and mixed precision
• Parameter-efficient fine-tuning concepts with adapters and LoRA
• Training run reproducibility using seeds, saved configurations and dataset versions

Workshop: Participants fine-tune a sequence classifier with the Trainer API and produce a saved checkpoint with its training configuration.

Day 4: Evaluation, error analysis and inference design

• Hugging Face Evaluate metrics and compute functions
• Precision, recall, F1 and support for imbalanced classification
• Confusion matrices and class-level performance interpretation
• Error-slice analysis by text length, source, label and confidence
• Token classification evaluation with sequence-alignment considerations
• Batch inference, device placement and pipeline configuration
• Latency, throughput and model-size trade-offs for operational inference

Workshop: Participants create an evaluation report that identifies high-risk error categories and recommends threshold, data or model changes.

Day 5: Release readiness and workplace application

• Model cards for intended use, limitations, data and evaluation evidence
• Inference scripts and serialisable tokenizer-model artefacts
• Safetensors, model revisions and controlled artefact retrieval
• Licensing and usage considerations for Hub models and datasets
• Bias, harmful output and sensitive-data risk assessment
• Acceptance criteria for model release and monitoring handover
• Use-case planning from business objective to data, metric and deployment decision

Workshop: Participants assemble and present a release-ready transformer-model package containing an inference script, model card, metric summary and implementation plan.

Tools & standards covered

Hugging Face Transformers, Hugging Face Datasets, Hugging Face Evaluate, Hugging Face Accelerate

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions