Hey r/programming,

Sharing an interesting engineering decision I made while building an AI complaint analysis system.

**The naive approach:**

Train one BERT model on all 51,000 reviews. Simple. Clean. One model to rule them all.

**The problem:**

A complaint about an Appliance:

"Motor making grinding noise after 3 months. Compressor seems faulty."

A complaint about Fashion:

"Colour faded after first wash. Stitching came apart."

A complaint about Electronics:

"Screen flickering randomly. Battery draining in 2 hours."

These are completely different vocabularies, failure modes, and complaint patterns. A single model trained on all of them learns a blurry average. Category-specific models learn the actual language of each domain.

**What I built:**

7 separate BERT models — one per product category.

```python

# Simplified training loop per category

for category in categories:

df_cat = df[df['category'] == category]

df_balanced = balance_classes(df_cat)

model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

train(model, df_balanced)

model.save_pretrained(f'models/{category}_bert')

```

**Results:**

- Electronics — 100% accuracy (5,870 rows)

- Appliances — 99% accuracy (27,314 rows)

- Home — 100% accuracy (2,720 rows)

- Fashion — 96% accuracy (2,244 rows)

**Interesting engineering challenges:**

1. Class imbalance — most reviews are positive. Fixed with equal sampling per class before training.

2. Memory on Mac CPU — 27K row Appliances model took 45 mins. Batch size of 16 was the sweet spot.

3. UNEXPECTED keys warning — normal when loading bert-base-uncased for classification. The pre-training MLM head gets replaced by a classification head. Safe to ignore.

4. Saving mid-epoch — save the best model per epoch using validation accuracy, not just the last epoch.

**Next step:**

CrewAI multi-agent layer on top — Classifier Agent, Pattern Agent, Priority Agent, Resolution Agent.

**GitHub:** github.com/niteshnankani-svg

Happy to discuss the category-specific vs single model tradeoff or the BERT fine-tuning pipeline.