Methoden vergelijken

	Vertaalde Vraagbeantwoording	Fijn-afgestelde BERT-gebaseerde Classificatie
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2016–2019	2019
Grondlegger	Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)
Type	Transfer learning / fine-tuning for extractive or generative QA	Pre-trained transformer fine-tuned for classification
Oorspronkelijke bron	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
Aliassen	fine-tuned QA, neural QA with fine-tuning, extractive QA fine-tuning, reading comprehension fine-tuning	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification
Verwant	5	5
Samenvatting	Fine-Tuned Question Answering adapts a large pre-trained language model — such as BERT, RoBERTa, or a GPT-family model — to answer natural-language questions over a given context passage or knowledge base. The model learns to locate answer spans or generate free-form answers by continuing training on labeled QA pairs after general-purpose pre-training.	Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.