Full Hugging Face with Python syllabus only covering Python parts that are directly relevant to Hugging Face usage (so no generic Python basics, since you’ll cover those separately).

Hugging Face with Python – Full Hands-On Syllabus

---

Section 1 – Introduction & Setup

Goal: Understand Hugging Face ecosystem, install and set up environment.

1. What is Hugging Face?

   • Transformers library

   • Datasets library

   • Tokenizers library

   • Model Hub

   • Spaces

2. Installation & Environment Setup

   • Installing transformers, datasets, huggingface_hub, evaluate

   • Python virtual environments (venv, conda) for HF projects

   • Setting up a free Hugging Face account and API token

3. First Hands-on: Load a pre-trained model and tokenizer.

   from transformers import pipeline
   classifier = pipeline("sentiment-analysis")
   print(classifier("Hugging Face is awesome!"))
   

Mini Project:
✅ Sentiment Analyzer – User enters text, script returns positive/negative with confidence score.

---

Section 2 – Transformers Fundamentals

Goal: Learn model loading, tokenization, pipelines, and inference.

1. Pre-trained Models & Pipelines

   • Text classification

   • Named Entity Recognition (NER)

   • Question Answering

   • Text Generation

2. Tokenizer Basics

   • Tokenization process (subword, wordpiece, BPE)

   • Encoding and decoding text

   • Padding, truncation, attention masks

3. Hands-on Exercises:

   • Load different models from transformers

   • Tokenize custom text and see token IDs

   • Decode token IDs back to text

Mini Project:
✅ Custom NER Tool – Extract names, locations, and organizations from text.

---

Section 3 – Working with Hugging Face Datasets

Goal: Learn to load, explore, preprocess, and split datasets.

1. Datasets Basics

   • Loading built-in datasets (IMDB, SQuAD, etc.)

   • Loading CSV/JSON from local or URL

   • Dataset structure: features, splits, metadata

2. Data Processing

   • Filtering, mapping, and formatting datasets

   • Tokenizing entire datasets efficiently (map with batched processing)

   • Train/validation/test split

3. Hands-on:

   • Load IMDB reviews dataset

   • Preprocess for classification task

Mini Project:
✅ Text Cleaner & Preprocessor – Load dataset, clean text, tokenize, and prepare for model training.

---

Section 4 – Fine-tuning Pre-trained Models

Goal: Learn to train and fine-tune models for custom tasks.

1. Training Setup

   • Understanding AutoModelForSequenceClassification

   • Trainer API basics

   • TrainingArguments configuration

2. Fine-tuning Steps

   • Load dataset → Tokenize → Model definition → Trainer → Train

   • Saving and loading fine-tuned models

3. Hands-on:

   • Fine-tune BERT for sentiment classification

   • Evaluate using evaluate library

Mini Project:
✅ Movie Review Sentiment Classifier – Fine-tune BERT on IMDB dataset.

---

Section 5 – Advanced Fine-tuning & Customization

Goal: Handle larger models, optimize training, and customize architectures.

1. Training Optimization

   • Mixed precision training

   • Gradient accumulation

   • Data collators for dynamic padding

2. Custom Models

   • Modify architecture layers

   • Use AutoModel for non-classification tasks

3. Saving & Sharing Models

   • Push to Hugging Face Hub with API token

   • Load directly from Hub in another project

Mini Project:
✅ Custom Toxic Comment Detector – Fine-tune DistilBERT with additional custom classification layers.

---

Section 6 – Beyond NLP: Vision & Audio Models

Goal: Explore non-text models in Hugging Face.

1. Image Models

   • Image classification pipeline (vit, resnet)

   • Image segmentation

2. Audio Models

   • Speech-to-text (whisper)

   • Audio classification

3. Hands-on:

   • Classify images using pipeline("image-classification")

   • Convert speech to text

Mini Project:
✅ Speech Sentiment Detector – Convert audio to text, run sentiment analysis.

---

Section 7 – Deployment & Integration

Goal: Deploy and use models in real applications.

1. FastAPI Integration – Create REST API for HF models

2. Streamlit/Gradio Apps – Interactive UIs

3. Hugging Face Spaces Deployment – Free hosting for models and apps

Mini Project:
✅ Text Summarization Web App – Users paste an article, model outputs a summary.

---

Section 8 – Advanced Topics & Research

Goal: Explore cutting-edge Hugging Face features.

1. PEFT & LoRA – Parameter-efficient fine-tuning for large models

2. Transformers for RAG – Retrieval-Augmented Generation with transformers + faiss

3. Multi-task models – Train one model for multiple tasks

4. Quantization & Model Optimization – Run large models on small hardware

Capstone Project Ideas:

• AI Customer Support Bot (QA + summarization + sentiment analysis)

• RAG-based PDF Question Answering System (LangChain + HF models)

• Multi-modal AI (text + image inputs)

---

📌 Teaching Flow Tip:

• Start each section with concepts, then code-along examples, then a small hands-on task.

• Include model training on free Google Colab to keep costs zero.

• End with a capstone project combining multiple Hugging Face capabilities.

---

Are you a BIT student struggling with your final year project or looking for expert guidance to ace your UCSC final year project? 💡 We've got you covered!  

✅ What We Offer:  

- Personalized project consultations  

- Step-by-step project development guidance  

- Expert coding and programming assistance (PHP, Python, Java, etc.)  

- Viva preparation and documentation support  

- Help with selecting winning project ideas  

📅 Class Schedules:  

- Weekend Batches: Flexible timings for working students  

- Online & In-Person Options  

🏆 Why Choose Us?  

- Proven track record of guiding top BIT projects  

- Hands-on experience with industry experts  

- Affordable rates tailored for students  

🔗 Enroll Now: Secure your spot today and take the first step toward project success!  

📞 Contact us: https://web.facebook.com/itclasssrilanka  

📍 Location: Online  

🌐 Visit us online: https://localedxcelcambridgeictcomputerclass.blogspot.com/

✨ Don't wait until the last minute! Start your BIT final year project with confidence and guidance from the best in the industry. Let's make your project a success story!  

---

Individual attention to all students 

🎓 Final Year IT & Computer Science Students! 🚀

Get Expert Guidance for Your Degree Projects & Reports!

📍 Colombo | Moratuwa | UoM | Colombo Uni | BIT | BIT Degree | SLIIT | BSc

💡 We Specialize In:

✅ Project Proposal Writing (University-Ready)

✅ Interim Report Assistance

✅ Final Dissertation / Use Case Document Support

✅ Test Manual & Installation Guides

✅ Complete Final-Year Project System (Fully Working)

✅ Test Document Preparation

✅ Computer Studies / BIT / Software Engineering / IT / VLE Courses

👨‍🏫 Supervision by Experienced Software Developers & University Project Guides

📘 Covers: Java, Python, PHP, MySQL, AI, Web & Mobile Development, Machine Learning

📞 Contact Now to Book Your Slot – Limited Project Guidance Available!

Section 1 – Introduction & Setup detailed notes in point format so they’re ready for Hugging Face

Section 1 – Introduction & Setup

Goal: Students understand what Hugging Face is, its ecosystem, and can set up a Python environment to start using it.

---

1.1 What is Hugging Face?

• Definition:

  • Hugging Face is an AI/ML platform and open-source ecosystem for Natural Language Processing (NLP), Computer Vision (CV), and Speech tasks.

  • Focuses on transformer-based models for deep learning.

• Core Components:

  1. Transformers Library

     • Python library for loading, training, and using pre-trained models.

     • Supports NLP, Vision, Audio, and Multimodal tasks.

     • Example tasks: Sentiment Analysis, Text Generation, Question Answering.

  2. Datasets Library

     • Ready-to-use datasets for training and testing models.

     • Efficient streaming and processing of large datasets.

  3. Tokenizers Library

     • Fast, language-specific text tokenization.

     • Implements modern tokenization algorithms like BPE, WordPiece, Unigram.

  4. Hugging Face Hub

     • Public repository for pre-trained models and datasets.

     • You can push your own models/datasets and share them.

  5. Spaces

     • Free hosting for AI apps (Gradio, Streamlit) connected to HF models.

• Why Hugging Face is Popular:

  • Large collection of ready-to-use models.

  • Easy integration with PyTorch and TensorFlow.

  • Active community & free resources.

---

1.2 Installation & Environment Setup

• Prerequisites:

  • Python 3.8+ installed.

  • Basic understanding of Python syntax.

• Step 1: Create a Virtual Environment

  • Keeps dependencies isolated from other projects.

  python -m venv hf_env
  source hf_env/bin/activate   # Mac/Linux
  hf_env\Scripts\activate      # Windows
  

• Step 2: Install Hugging Face Libraries

  pip install transformers datasets tokenizers evaluate huggingface_hub
  

• Step 3: Optional – GPU Setup for Faster Processing

  • Install PyTorch or TensorFlow with GPU support (CUDA).

  • Example for PyTorch:

    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
    

• Step 4: Create a Hugging Face Account

  • Go to https://huggingface.co

  • Sign up (free).

  • Get your Access Token from Settings → Access Tokens.

• Step 5: Login via CLI (Optional for Model Upload)

  huggingface-cli login
  

---

1.3 First Hands-on – Load a Pre-trained Model

• Using Pipeline API:

  from transformers import pipeline
  classifier = pipeline("sentiment-analysis")
  result = classifier("Hugging Face is awesome!")
  print(result)
  # [{'label': 'POSITIVE', 'score': 0.9998}]
  

• Concepts Introduced:

  • Pipeline: High-level API for running models without detailed setup.

  • Task Name: "sentiment-analysis", "text-generation", "translation", etc.

  • Output: Structured dictionary with labels and scores.

---

1.4 Mini Project – Simple Sentiment Analyzer

• Description:

  • Take user input from the console.

  • Pass it to the Hugging Face sentiment analysis pipeline.

  • Output sentiment & confidence score.

• Code Example:

  from transformers import pipeline
  
  sentiment_pipeline = pipeline("sentiment-analysis")
  
  while True:
      text = input("Enter text (or 'quit' to exit): ")
      if text.lower() == "quit":
          break
      result = sentiment_pipeline(text)[0]
      print(f"Sentiment: {result['label']}, Confidence: {result['score']:.2f}")
  

• Learning Outcomes:

  • Running pre-trained models in Python.

  • Understanding HF’s high-level workflow.

  • Introduction to model inference without training.

---

Are you a BIT student struggling with your final year project or looking for expert guidance to ace your UCSC final year project? 💡 We've got you covered!  

✅ What We Offer:  

- Personalized project consultations  

- Step-by-step project development guidance  

- Expert coding and programming assistance (PHP, Python, Java, etc.)  

- Viva preparation and documentation support  

- Help with selecting winning project ideas  

📅 Class Schedules:  

- Weekend Batches: Flexible timings for working students  

- Online & In-Person Options  

🏆 Why Choose Us?  

- Proven track record of guiding top BIT projects  

- Hands-on experience with industry experts  

- Affordable rates tailored for students  

🔗 Enroll Now: Secure your spot today and take the first step toward project success!  

📞 Contact us: https://web.facebook.com/itclasssrilanka  

📍 Location: Online  

🌐 Visit us online: https://localedxcelcambridgeictcomputerclass.blogspot.com/

✨ Don't wait until the last minute! Start your BIT final year project with confidence and guidance from the best in the industry. Let's make your project a success story!  

---

Individual attention to all students 

🎓 Final Year IT & Computer Science Students! 🚀

Get Expert Guidance for Your Degree Projects & Reports!

📍 Colombo | Moratuwa | UoM | Colombo Uni | BIT | BIT Degree | SLIIT | BSc

💡 We Specialize In:

✅ Project Proposal Writing (University-Ready)

✅ Interim Report Assistance

✅ Final Dissertation / Use Case Document Support

✅ Test Manual & Installation Guides

✅ Complete Final-Year Project System (Fully Working)

✅ Test Document Preparation

✅ Computer Studies / BIT / Software Engineering / IT / VLE Courses

👨‍🏫 Supervision by Experienced Software Developers & University Project Guides

📘 Covers: Java, Python, PHP, MySQL, AI, Web & Mobile Development, Machine Learning

📞 Contact Now to Book Your Slot – Limited Project Guidance Available!

Hugging Face Website – Beginner Explanation very simple beginner-friendly language so that even if you are new, you can understand Hugging Face step by step. Think of Hugging Face as a big online library + playground for AI and Machine Learning.

 Perfect 👌 I’ll explain everything in 

---

🌍 

---

🔹 1. Models – Main

• Imagine a supermarket for AI models.

• Here, you can find ready-made brains (AI models) trained by experts.

• You don’t always need to train a model yourself — you can just pick one and use it.

• Example:

  • Want to translate English → Sinhala? → Use a translation model.

  • Want to recognize objects in pictures? → Use an image model.

👉 Models save you time because someone already did the hard work of training.

---

🔹 2. Tasks

• “Tasks” are simply what job you want the AI to do.

• Example tasks:

  • NLP (Text) → translate text, summarize text, answer questions, chatbots.

  • Vision (Images) → identify cats/dogs, detect objects, generate images.

  • Audio (Sound) → convert speech → text, or text → speech.

• Hugging Face organizes models by tasks so beginners can just say:
  “I want summarization” → it shows you all models that can summarize.

---

🔹 3. Libraries

• These are toolkits that help you use models easily in Python.

• Main Hugging Face libraries:

  • Transformers → Use pre-trained models for text, images, and audio.

  • Datasets → Load and use huge datasets with 1 line of code.

  • Tokenizers → Break down text into smaller pieces (important for AI).

  • Diffusers → Generate images with AI (like Stable Diffusion).

  • Accelerate → Helps train models faster on GPU/TPU.

👉 Think of libraries as apps in your phone → each one helps with a specific thing.

---

🔹 4. Languages

• There are two types of languages here:

  1. Programming languages → Most models are used with Python.

  2. Human languages → Models are tagged with languages they understand.

     • Example: Some models only work in English, some in Sinhala, Tamil, Hindi, etc.

👉 So, if you need a Sinhala speech recognition model → you can filter by language.

---

🔹 5. Licenses

• A license tells you what you are allowed to do with a model or dataset.

• Some are open and free (MIT, Apache 2.0).

• Some have rules (CC-BY → give credit, Non-commercial → not for business use).

• Example:

  • If you are making a personal project → most open-source models are fine.

  • If you are building a business → you must check the license.

👉 Always check the license before using a model in a company project.

---

🔹 6. Other

• This is like the extra features section of Hugging Face.

• Includes:

  • Leaderboards → Which models are best at a certain task.

  • Collections → Groups of models/datasets.

  • Papers → Research connected to models.

• Helps you see which model is most accurate or popular.

---

🔹 7. Datasets

• A dataset = collection of data used to train or test AI.

• Hugging Face has a Datasets Hub → you can download thousands of datasets.

• Examples:

  • IMDB movie reviews (for sentiment analysis).

  • Wikipedia (for text understanding).

  • ImageNet (for image recognition).

• Instead of searching on the internet, Hugging Face gives you ready-to-use datasets.

👉 You can directly load them with Python code.

---

🔹 8. Spaces

• Spaces = Mini websites/apps where people share live demos of their AI models.

• Made with Gradio or Streamlit (simple Python tools).

• Example Spaces:

  • A chatbot you can talk to.

  • An image generator where you type text, and it makes a picture.

  • A speech-to-text demo where you upload audio, and it gives text.

👉 You don’t need to install anything — just click and try it.

---

🔹 9. Community

• Hugging Face is not just tools — it’s also a big community of AI learners and experts.

• Community includes:

  • Discussions & forums → Ask questions, get answers.

  • Organizations → Teams or companies sharing their models.

  • Contributions → You can also upload your model/dataset and share.

👉 If you’re stuck, the community can help you learn and grow.

---

🎯 Beginner Summary (Super Simple)

• Models → Ready-made AI brains.

• Tasks → Jobs for AI (translate, summarize, detect objects, etc.).

• Libraries → Toolkits like Transformers, Datasets, Diffusers.

• Languages → Models tagged by human & programming language.

• Licenses → Rules for using models.

• Other → Extra features (leaderboards, papers).

• Datasets → Collections of data for AI.

• Spaces → Free apps/demos to try AI.

• Community → People to learn and share with.

---

👉 Would you like me to also create a real-life analogy (like comparing Hugging Face to a “School” or “Supermarket”) so you can remember these 9 sections more easily?

🚀 What is a Pipeline in Hugging Face? Tutorials Classes Examples

🚀 What is a Pipeline in Hugging Face?

A pipeline is like a shortcut tool in Hugging Face that lets you use powerful AI models with just a few lines of code, without worrying about all the technical setup.

👉 Think of it as a ready-made machine:

• You give input → It processes with a model → You get output.

• No need to manually load models, tokenizers, or preprocess data.

---

🛠️ Example in Real Life:

Imagine you go to a juice shop:

• You give fruits (input).

• The shop’s juicer (pipeline) automatically washes, peels, and squeezes.

• You get juice (output).

You don’t need to know how the juicer works inside — just enjoy the juice.
Similarly, Hugging Face pipelines let you focus on results, not coding complexity.

---

🖥️ Example in Hugging Face (Python code):

from transformers import pipeline

# Create a pipeline for sentiment analysis
classifier = pipeline("sentiment-analysis")

# Give it text
result = classifier("I love Hugging Face, it's amazing!")

print(result)

✅ Output:

[{'label': 'POSITIVE', 'score': 0.9998}]

👉 Here:

• Task: Sentiment Analysis (find if text is positive/negative).

• Pipeline: Handles everything (loading model + tokenizer + processing).

• Result: It tells you the text is positive with 99% confidence.

---

🔹 Types of Pipelines (Tasks you can run)

Some common Hugging Face pipelines are:

• "sentiment-analysis" → Positive or negative feeling.

• "text-generation" → Continue writing text (like GPT).

• "translation" → Translate text between languages.

• "summarization" → Summarize long text into short.

• "question-answering" → Ask a question, get an answer from text.

• "image-classification" → Detect what’s in a picture.

• "automatic-speech-recognition" → Convert speech → text.

---

🎯 Why is Pipeline Useful for Beginners?

• ✅ Very easy to use (few lines of code).

• ✅ No need to understand all internal details.

• ✅ Great for trying out models quickly.

• ✅ Supports many different tasks.

Later, if you want more control and customization, you can directly use AutoModel and AutoTokenizer. But for starting, pipeline is the easiest entry point.

---

Build an order management system. Easily track orders, vendors, products, and more with a user-friendly dashboard BIT UCSC UOM Software Project Managment

 Focusing on Efficiency and Speed

"Still drowning in spreadsheets and paperwork? It’s time to automate those manual jobs and reclaim your time." "Stop working for your processes. Start making your processes work for you with a custom system." "Generic software can't fix your specific bottlenecks. Discover the power of a system built just for your manual jobs." "Automate the grind. Free up your team to focus on growth, not manual entry." Highlighting Cost and Errors These hooks appeal to the business sense of the user, focusing on the hidden costs of manual labor. "Manual jobs are costing you more than just time. Eliminate errors and unlock efficiency with a custom software solution." "Is manual oversight slowing your ROI? Get a bespoke system designed to streamline your unique workflow." "Stop patching generic software. Invest in a seamless custom system that handles your exact manual jobs without compromise." Focusing on Business Growth and Competitive Edge These hooks aim higher, linking customized systems to a strategic advantage. "If your process is unique, your software should be too. Gain a competitive edge with a customized system for your manual jobs." "Future-proof your operations. Outgrow the limits of manual labor with custom-built automation." "Don't just digitize your manual jobs—optimize them. Get a system built precisely for the way you work." Best "All-Around" Option This is a strong, versatile option: How to make order tracking system PHP mySQL online order tracking system order tracking software customer order tracking software simple order management system how to create an order management system order processing software small business order management system order system for small business order management software order management app

💥 WordPress https://computerclassinsrilanka.wordpress.com 💥 Facebook https://web.facebook.com/itclasssrilanka 💥 Wix https://itclasssl.wixsite.com/icttraining 💥 Web https://itclasssl.github.io/eTeacher/ 💥 Medium https://medium.com/@itclasssl 💥 Quora https://www.quora.com/profile/BIT-UCSC-UoM-Final-Year-Student-Project-Guide 💥 mystrikingly https://bit-ucsc-uom-final-year-project-ideas-help-guide-php-class.mystrikingly.com/ 💥 https://elakiri.com/threads/bit-ucsc-uom-php-mysql-project-guidance-and-individual-classes-in-colombo.1627048/ 💥 https://bitbscucscuomfinalprojectclasslk.weebly.com/ 💥 https://www.tiktok.com/@onlinelearningitclassso1 💥 https://payhip.com/eTeacherAmithafz/ 💥 https://discord.gg/cPWAANKt 💥 https://6789f6ca695da.site123.me/blog 💥 https://itclasssl.github.io/eTeacher/ 💥 https://www.tumblr.com/blog/computercoursesincolombo-blog 💥Subscribe on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7072056309516304384 💥https://www.scribd.com/user/682883198/Online-Learning-IT-Class-Software-Dev 🚀 English Sinhala Tamil Medium Online Classes ✅ Final Year Software Web Project Guidance BIT UCSC UoM ✅ Grade 1 to GCE O/L A/L ICT GIT Classes ✅ PHP & Python Training ✅ Web & Software Development ✅ Social Media Marketing 📲 Learn, Build & Succeed! Join us today! 🚀