Linganisha mbinu

	Uainishaji unaotegemea usimamizi dhaifu wa BERT	Uainishaji wa BERT Ulioboreshwa
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2017–2020	2019
Mwanzilishi	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)
Aina	Weakly supervised fine-tuning of pre-trained language model	Pre-trained transformer fine-tuned for classification
Chanzo asilia	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. Proceedings of NAACL-HLT 2019, 4171–4186.
Majina mbadala	WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification
Zinazohusiana	6	5
Muhtasari	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.	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.