विधियों की तुलना करें

	दुर्बल पर्यवेक्षित ट्रांसफार्मर	BERT-आधारित वर्गीकरण
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017–2019	2019
प्रवर्तक	Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
प्रकार	Weakly supervised deep learning	Pre-trained language model with fine-tuning
मौलिक स्रोत	Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid training data creation with weak supervision. Proceedings of the VLDB Endowment, 11(3), 269–282.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
उपनाम	WST, weakly supervised attention model, noisy-label transformer, weak supervision with transformers	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
संबंधित	5	4
सारांश	Weakly Supervised Transformer combines the representational power of Transformer architectures with weak supervision strategies that exploit noisy, incomplete, or programmatically generated labels — making it possible to train high-quality NLP and vision models when fully annotated datasets are scarce or prohibitively expensive to produce.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.