Natural Language Processing using Hugging Face
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
5.5 Level NCrF
National Credit Framework
90 Hours
Flexible Learning
Beginner Level
No prior experience required
Micro Credit Course
03 Credit
Course Overview
This course offers a beginner-friendly introduction to Large Language Models (LLMs) and practical Natural Language Processing (NLP) through hands-on learning. Students work with real-world text datasets, gaining an intuitive understanding of how models like BERT can be fine-tuned for tasks such as sentiment analysis and text classification using the Hugging Face platform. By the end of the course, learners will have the skills to fine-tune pre-trained transformer models and build simple NLP applications using Python in cloud-based environments.
Key Learning Highlights
Introduction to Large Language Models and transformer-based architectures like BERT
Hands-on experience fine-tuning pre-trained models using Hugging Face Transformers
Working with real-world text datasets for tasks like sentiment analysis and text classification
Understanding tokenization, encoding, and dataset preparation for NLP workflows
Evaluating model performance using precision, recall, and F1-score
Deploying fine-tuned models using cloud-based tools and inference APIs
Tools & Platforms
Python
Hugging Face
PyTorch
TensorFlow
Jupyter
Streamlit
Gradio
Learning Outcome
By the end of this course, students will be able to:
Understand key NLP concepts, transformer architectures, and the working mechanism of models like BERT and RoBERTa.
Fine-tune publicly available pre-trained transformer models for custom downstream tasks.
Build and evaluate NLP applications such as sentiment analysis, text classification, and named entity recognition (NER).
Prepare, preprocess, manage and handle text datasets and efficiently train models using the Hugging Face platform.
Create full NLP workflows and deploy end-to-end pipelines suitable for real-world use cases.
Master the course with just 5 Modules
Master this course in just 5 focused modules — starting from the basics of NLP and transformers to hands-on fine-tuning using real datasets. Learn to use Hugging Face tools for training, optimizing, and evaluating powerful models like BERT. Finally, build and deploy your own NLP applications with simple UI tools like Streamlit and Gradio.
Introduction to NLP and Transformers
- Introduction to NLP tasks: text classification, tokenization, embeddings
- Overview of transformer architectures (BERT, GPT, RoBERTa)
- Introduction to Hugging Face Transformers library
Model Fine-Tuning with Hugging Face
- Loading pre-trained models (e.g., BERT, DistilBERT)
- Tokenization, model architecture, classification heads
- Fine-tuning for text classification tasks
Applied Text Analytics
- Working with real-world datasets (e.g., IMDB, Yelp, AG News)
- Sentiment analysis and text classification workflows
- Model training, evaluation metrics (accuracy, F1 score)
Sequence Labeling and Question Answering
- Named Entity Recognition (NER) using Hugging Face pipelines
- Question Answering with models fine-tuned on SQuAD
- Evaluation using precision, recall, and span-based accuracy
Capstone Project – End-to-End NLP Application
- Define a real-world NLP problem (e.g., fake news detection, chatbot, resume screening)
- Collect and preprocess text data
- Fine-tune a model and build an inference pipeline
- Deploy the model with Streamlit or Gradio
Submit a working solution with documentation and video demo
Roles
- NLP Engineer
- Chatbot Developer
