By completing this course, learners will:

1. Understand the transformer architecture and its impact on NLP evolution.
2. Utilize Hugging Face libraries (Transformers, Datasets, Tokenizers) effectively.
3. Load, fine-tune, and evaluate pretrained models like BERT, GPT, RoBERTa, and T5.
4. Perform tokenization, data preparation, and handle large-scale datasets.
5. Apply transformers to real-world tasks such as sentiment analysis, NER, QA, and summarization.
6. Optimize performance using quantization, mixed precision, and Trainer API.
7. Serve models using Hugging Face Inference API and ONNX/TensorRT.
8. Explore multimodal models like CLIP and DALL·E and future trends in LLMs.

Course Syllabus

Module 1: Introduction to Transformers and Hugging Face

• Evolution of NLP, Transformer architecture, Hugging Face ecosystem, Pretrained models (BERT, GPT, RoBERTa, T5)

Module 2: Tokenization and Data Preparation

• Tokenization (WordPiece, BPE, SentencePiece), Tokenizers library, Dataset loading and processing

Module 3: Fine-Tuning Pretrained Models

• Text Classification (BERT for sentiment analysis)
• Named Entity Recognition (NER)
• Question Answering (SQuAD datasets)

Module 4: Advanced Use Cases and Customization

• Text Generation (GPT, T5), Summarization (T5), Translation (mBART), Custom layers and architecture

Module 5: Performance Optimization

• Mixed precision, Trainer API, Quantization with BitsAndBytes, ONNX, TensorRT, Model serving with Inference API

Module 6: Multimodal Models and Future Trends

• CLIP, DALL·E, Image-text retrieval, VQA, Generative AI and LLM trends

Module 7: Capstone Project and Certification

• Real-world project (e.g., chatbot, QA system), Implementation, Quiz and coding assessment

Upon successful completion of the Hugging Face Transformers for NLP and LLM Applications course, learners will receive a Course Completion Certificate from Uplatz, demonstrating their skills in building, fine-tuning, and deploying state-of-the-art NLP and LLM solutions.

This certificate serves as a valuable proof of your ability to work with Hugging Face tools and advanced transformer-based models. It can boost your profile for roles in AI/ML, data science, and NLP engineering.

The course also helps learners prepare for interviews and advanced certifications in AI and machine learning, including Hugging Face’s own certificate programs and broader industry-recognized NLP certifications.

Career & Jobs

The demand for professionals skilled in NLP and LLMs is soaring. Hugging Face is at the forefront of AI innovation, making this course highly relevant for a wide range of AI careers.

Career Opportunities After This Course

1. NLP Engineer
2. AI/ML Developer
3. Data Scientist (NLP Focus)
4. LLM Developer or Researcher
5. Machine Learning Engineer
6. AI Consultant (Language Models)
7. Research Assistant in Generative AI

Industries Hiring NLP/LLM Experts

• AI Research Labs
• Tech & Software Companies
• E-commerce & Customer Support Platforms
• Healthcare & Bioinformatics
• Financial Services & Risk Analytics

1. What is a transformer model and why is it effective in NLP?
Transformers use self-attention mechanisms to model relationships between all words in a sentence simultaneously, enabling better context understanding and parallelization.

2. What are pretrained models in NLP?
Pretrained models are transformer architectures trained on large corpora (like BERT, GPT) and fine-tuned for specific tasks, reducing the need for task-specific model building.

3. What are tokenizers and why are they important?
Tokenizers break text into tokens that models can understand. Hugging Face supports WordPiece, BPE, and SentencePiece formats.

4. How do you fine-tune a model using Hugging Face?
Use the Trainer API with labeled data, define training arguments, and supply a model checkpoint to adapt to your specific NLP task.

5. What is the difference between top-k and top-p sampling in text generation?
Top-k limits choices to the top k probable tokens, while top-p (nucleus sampling) includes tokens until cumulative probability exceeds p.

6. How does Hugging Face handle large models during training?
It supports model parallelism, quantization (BitsAndBytes), mixed-precision training (with accelerate), and model sharding.

7. What are CLIP and DALL·E models?
CLIP connects images and text in a shared embedding space; DALL·E generates images from text. Both are multimodal transformer models.

8. How do you deploy a Hugging Face model?
Use the Inference API or convert to ONNX for serving with optimized runtimes like TensorRT.

9. What are practical applications of Hugging Face models in enterprises?
Chatbots, sentiment analysis, document summarization, QA systems, translation, and content generation.

1. Who is this course for?
Data scientists, NLP engineers, AI developers, and researchers looking to build applications using transformer models and LLMs.

2. Is any prior experience needed?
Basic Python and ML knowledge is helpful. Prior NLP experience is a plus but not mandatory.

3. Do I need a GPU to practice?
While GPUs are beneficial, most exercises can run on CPU or via Google Colab with GPU support.

4. What is the course format?
Self-paced video lessons, coding assignments, quizzes, and a capstone project.

5. Will I get a certificate?
Yes, you’ll receive a Uplatz Course Completion Certificate upon successfully completing the course and project.

6. Does this course help with Hugging Face certifications?
Yes. It prepares you for Hugging Face’s certification paths and broader NLP interviews.

7. Can I use this course in my job or research?
Absolutely. The course includes real-world applications and deployment strategies.