Hugging FaceNLP with Transformers (Hugging Face) Training
Fine-tune, evaluate and deploy transformer models for real NLP tasks with Hugging Face
- Duration
- Live batches
- Format
- Live online
- Modules
- 10 modules
- Certificate
- On completion
- ✓Live online instructor-led training in scheduled batches
- ✓Hugging Face Transformers, Datasets, Tokenizers and the Hub
- ✓Fine-tune encoder and small decoder models for classification, NER, QA and summarisation
- ✓Parameter-efficient fine-tuning with LoRA / PEFT on free GPUs
- ✓Evaluation, error analysis and deployment of NLP models
- ✓Assure Success course-completion certificate after a capstone
About the course
What this course covers
NLP with Transformers (Hugging Face) is a hands-on course for practitioners who want to go beyond calling an API and actually adapt transformer models to their own data. You learn how tokenisation, attention and pretrained checkpoints fit together, then use the Hugging Face stack to fine-tune models for tasks such as classification, token tagging, extractive question answering, summarisation and embedding-based retrieval.
A large part of the course is about doing this efficiently and responsibly. You use LoRA and other parameter-efficient methods to fine-tune on a free GPU, build proper train, validation and test splits, choose metrics that match the task, and run error analysis. You also compare a fine-tuned small model against a prompted LLM so you can justify the choice on accuracy, cost, latency and privacy.
There is no external exam. The course ends with a capstone and an Assure Success course-completion certificate.
Who should attend
- ● Data scientists and ML engineers working on text problems
- ● Developers who completed NLP with Python and want to go deeper
- ● AI engineers building search, extraction or classification systems
- ● Researchers and graduate students who want a practical transformer workflow
- ● Teams with domain text (legal, finance, healthcare, support) that generic models handle poorly
Prerequisites
- Solid Python and basic machine learning knowledge
Key skills
Skills you will walk away with
Transformer architecture
Explain self-attention, encoder, decoder and encoder-decoder models and their use cases.
Tokenisation
Understand subword tokenisers and handle special tokens, truncation and padding.
Fine-tuning for classification and tagging
Adapt pretrained encoders to text classification and named entity recognition.
Question answering and summarisation
Fine-tune and evaluate models for extractive QA and abstractive summaries.
Parameter-efficient fine-tuning
Use LoRA / PEFT and quantisation to train larger models with limited memory.
Embeddings and retrieval
Create sentence embeddings and build semantic search over your own documents.
Curriculum
Module-by-module syllabus
10 modules · 40 topics
1Module 1: Transformer Foundations4 topics ▾
Learning objective: Understand how transformers process language.
- ▸ Self-attention and positional information
- ▸ Encoder, decoder and encoder-decoder families
- ▸ Pretraining vs fine-tuning
- ▸ Hugging Face Hub and model cards
2Module 2: Tokenisers and Datasets4 topics ▾
Learning objective: Prepare text data correctly.
- ▸ BPE, WordPiece, SentencePiece
- ▸ Hugging Face Datasets
Schedules
Upcoming live batches
Enroll now and choose your batch dates
Send an enquiry and we'll reserve your seat in the next batch.
Certification
How you get certified
- 1Enrol in a scheduled live batch and confirm prerequisites with the coordinator
- 2Attend sessions and complete the fine-tuning notebooks
- 3Complete the capstone and present results for review
- 4Meet the attendance and assessment criteria for your batch
- 5Receive your Assure Success course-completion certificate
Career outcomes
Roles this opens up
NLP Engineer
₹8–20 LPA (approx.)Fine-tunes and deploys language models for domain text.
Applied ML / AI Engineer
₹8–22 LPA (approx.)Builds language features in products using pretrained and fine-tuned models.
Data Scientist (NLP)
₹7–18 LPA (approx.)Solves text classification, extraction and search problems.
Search / Relevance Engineer
₹8–20 LPA (approx.)Improves retrieval quality with embeddings and re-ranking.
Salary ranges are approximate figures for India and vary by city, company and experience.
Why this certification is worth it
Practical, not just API calls
You learn to adapt models to your data rather than depending only on prompts.
Why Assure Success
Learn it once, pass it once
Trainers from the field
Every session is led by a practitioner who uses this material at work.
Batches that fit your week
Weekday and weekend batches in IST, scheduled for working professionals.
Exam-style practice
Practice questions and, where available, a full simulator free with the course.
Exam voucher help
Discounted vouchers and step-by-step help booking your exam.
Human support
WhatsApp support Mon–Sat, 9 AM–8 PM IST — a person answers.
Pay your way
UPI, cards, netbanking and EMI in India; international cards accepted.
FAQs
Questions learners ask us
Q.Why fine-tune when I can just prompt an LLM?
Prompting is fastest to start. Fine-tuning a smaller model can be cheaper at scale, faster, easier to host privately and more consistent on a narrow task. The course teaches you to measure both and choose.
Q.Do I need a powerful GPU?
No. Exercises are designed for free GPU notebooks, using small models and LoRA where needed.
Q.What background should I have?
Python, basic machine learning and some NLP exposure. Prior PyTorch use is helpful but not mandatory.
Q.Is this the same as the Large Language Model (LLM) course?
No. This course is task-focused fine-tuning of transformer models with Hugging Face. The LLM course covers large model architecture, training and production applications more broadly.
Q.Will we work with Indian languages?
We discuss multilingual models and Indic-language options, and you can choose a multilingual dataset for your capstone if you have one.
Q.Is there an exam?
No external exam. You receive an Assure Success course-completion certificate.
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NLP with Transformers (Hugging Face) Training