Lab – BERT Fine-Tuning

Lab – BERT Fine-Tuning#

⏳ Loading Pyodide…

“Welcome to the moment you realize… fine-tuning a Transformer is easier than assembling IKEA furniture.” 🪑🧠

🧭 Lab Objective#

You’ll fine-tune a pre-trained Hugging Face Transformer (like distilbert, bert, or even llama) for a business-relevant NLP task — sentiment analysis, intent detection, or support ticket classification.

By the end:

You’ll have your own mini ChatGPT (without needing a GPU farm).
You’ll know how to train, evaluate, and deploy an LLM model.
You’ll earn bragging rights: “Yeah, I trained an AI. No big deal.” 😏

🧩 Choose Your Adventure (a.k.a. Project)#

Project Idea	Description	Example Model
💬 Customer Sentiment Classifier	Predict if a customer review is positive, neutral, or negative.	`bert-base-uncased`
📧 Email Intent Detection	Classify emails as sales inquiry, support issue, or spam.	`roberta-base`
🛒 Product Recommendation Q&A	Fine-tune a model to answer product questions.	`distilbert-base-cased`
💼 Resume Screening Bot	Rate resumes based on skill match.	`deberta-base`
🤓 Business Lingo Translator	Translate buzzwords (“synergy”, “ideation”) into plain English.	`t5-small`

⚙️ Step 1: Setup Environment#

pip install torch torchvision torchaudio transformers datasets evaluate accelerate

If you see “CUDA not available” — don’t panic. You’re just joining 90% of data scientists who secretly use Google Colab. 😆

🧠 Step 2: Load Dataset#

We’ll use a simple dataset for demonstration — you can swap in your own CSV later (like customer reviews or support tickets).

Output:

{'text': 'I loved this movie! The plot was amazing...', 'label': 1}

🪄 Step 3: Tokenize#

Let’s turn words into tensors (the only language Transformers understand).

🏋️ Step 4: Load Model and Trainer#

🧨 Step 5: Train Your Model#

🔥 Pro Tip: Training BERT on CPU is like brewing coffee with a candle. Use GPU if possible, or grab Colab’s free T4.

📊 Step 6: Evaluate Performance#

Output might look like:

{'eval_loss': 0.35, 'eval_accuracy': 0.88}

Translation: Your model understands English better than some of your coworkers. 😎

🧪 Step 7: Try Your Model#

Output:

[{'label': 'POSITIVE', 'score': 0.98}]

✅ Success! Your AI now understands emotions (take that, your ex).

🧠 Bonus Challenge: Business LLM Project#

Build your own domain-specific LLM:

Load t5-base or flan-t5-base
Train it on your company documents, FAQs, or policies

Ask it questions like:

"What’s our refund policy for digital subscriptions?"

Watch it reply like an HR-approved chatbot.

⚡ Common Pitfalls (and Funny Fixes)#

Problem	Solution	Comment
`CUDA out of memory`	Use smaller batch size	Or buy a GPU. Or a small island.
Model doesn’t learn	Lower learning rate	Or try praying.
Accuracy stuck at 50%	Check labels	Classic “train on garbage” problem.
Weird outputs	Tokenizer mismatch	Transformers hate identity crises.

💬 Deliverable#

🎯 Train & evaluate a Hugging Face Transformer for a real-world business task. 📊 Submit:

Code notebook
3 example predictions
A 1-line summary of what your model can do

Example:

“Our BERT model correctly detects angry customers before they call legal.” 😅

🧠 Reflection#

What did your model learn well?
Where did it fail?
How would you improve it with more data or better labels?

Remember:

Fine-tuning isn’t about perfection — it’s about progress… and GPUs not catching fire. 🔥

🧩 Summary#

Step	Task	Tool
1	Setup	`transformers`, `datasets`
2	Load Data	`load_dataset()`
3	Tokenize	`AutoTokenizer`
4	Train	`Trainer`, `TrainingArguments`
5	Evaluate	`evaluate()`
6	Predict	`pipeline()`
7	Profit	💰

“You didn’t just train a model. You trained an employee that never sleeps, complains, or asks for a raise.” 💼🤖

# Your code here