विधियों की तुलना करें

	अर्ध-पर्यवेक्षित BERT-आधारित वर्गीकरण	फाइन-ट्यून्ड BERT-आधारित वर्गीकरण
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2019–2020	2019
प्रवर्तक	Multiple groups (Xie et al.; Chen et al.; Devlin et al. for BERT base)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)
प्रकार	Semi-supervised fine-tuning of pre-trained transformer	Pre-trained transformer fine-tuned for classification
मौलिक स्रोत	Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792.	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.
उपनाम	Semi-supervised BERT, BERT SSL Classification, BERT with Unlabeled Data, BERT Semi-supervised Fine-tuning	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification
संबंधित	6	5
सारांश	Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation is scarce.	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.