Salīdzināt metodes

	BERT klasifikācija ar vājo uzraudzību	BERT klasifikācija
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017–2020	2019
Autors	Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Tips	Weakly supervised fine-tuning of pre-trained language model	Pre-trained language model with fine-tuning
Pirmavots	Meng, Y., Zhang, Y., Huang, J., Xiong, C., Ji, H., Zhang, C., & Han, J. (2020). Text Classification Using Label Names Only: A Language Model Self-Training Approach. Proceedings of EMNLP 2020, 9006–9017.	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.
Citi nosaukumi	WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Saistītās	6	4
Kopsavilkums	Weakly supervised BERT-based classification adapts BERT to text classification tasks when only noisy, heuristic, or programmatically generated labels are available instead of clean human annotations. It combines weak supervision frameworks — such as labeling functions and data programming — with BERT's pre-trained language representations to achieve robust classification without expensive hand-labeling.	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.