Lab – BERT Fine-Tuning

Lab – BERT Fine-Tuning#

“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).

from datasets import load_dataset

dataset = load_dataset("imdb")  # Movie reviews dataset
dataset["train"][0]

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).

from transformers import AutoTokenizer

model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)

def tokenize(batch):
    return tokenizer(batch["text"], padding=True, truncation=True)

dataset = dataset.map(tokenize, batched=True)
dataset.set_format("torch", columns=["input_ids", "attention_mask", "label"])

🏋️ Step 4: Load Model and Trainer#

from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments

model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    num_train_epochs=2,
    weight_decay=0.01,
    logging_dir="./logs"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"].select(range(2000)),  # small sample
    eval_dataset=dataset["test"].select(range(500))
)

🧨 Step 5: Train Your Model#

trainer.train()

🔥 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#

metrics = trainer.evaluate()
print(metrics)

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#

from transformers import pipeline

pipe = pipeline("text-classification", model="./results/checkpoint-1000")

pipe("This product changed my life! Highly recommend.")

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
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