Continuing training of a pre-trained model on a smaller, task-specific dataset to specialise its behaviour — the standard way DEJAN adapts base models into production classifiers.
Fine-tuning takes a model that has already been trained on a large general corpus — a pre-trained model — and continues training it on a smaller, task-specific dataset. The model's weights, which encode broad language understanding, are updated further to make the model excel at one particular job: classifying sentiment, detecting AI-generated text, predicting link placement, or anything else with labelled examples.
Starting from a pre-trained base means the model already understands language. Fine-tuning only needs to redirect that understanding toward the target task. This requires far less data and compute than training from scratch. A model pre-trained on billions of tokens can often be fine-tuned to production quality on thousands or tens of thousands of labelled examples, in hours rather than weeks.
Most of DEJAN's production models are fine-tuned rather than trained from scratch. LinkBERT is a fine-tuned BERT. LinkjeBERT is a fine-tuned mDeBERTa-v3. The AI Content Detection model is a fine-tuned DeBERTa-v3. The Query Intent Classifier is a fine-tuned ALBERT. The pattern is the same each time: take a strong pre-trained encoder, fine-tune it on high-quality labelled data for a specific SEO task, evaluate rigorously, deploy.
The key decisions during fine-tuning are the hyperparameters — learning rate, epoch count, batch size — and the quality and balance of the training data. A well-balanced dataset and a carefully tuned learning rate matter far more than the number of training steps.
Standard fine-tuning updates all the model's weights. Parameter-efficient methods like LoRA instead train small adapter matrices and freeze the original weights, drastically reducing the memory and compute required. For very large models, parameter-efficient fine-tuning is often the only practical option.
