News Classification: Classical Models and BERT
A comparison that asks when a simpler model is already enough.
Compared TF IDF based classifiers with a fine tuned BERT model on a labeled news article dataset. The project focused on the difference between a strong classical baseline, feature engineering, and transformer based classification.
The useful result of a benchmark is not that one model wins. It is understanding the cost and benefit of each approach on the same problem and data.
The comparison
The experiments covered Logistic Regression, Naive Bayes, SVM, Random Forest, and BERT. The same task and evaluation structure made the comparison meaningful instead of treating the transformer as an automatic answer.
The lesson
Classical models can be easier to train, inspect, and deploy. A transformer becomes useful when the added representation and transfer learning justify its cost.
selected tools
- Python
- scikit learn
- TF IDF
- BERT
- Hugging Face