পদ্ধতির তুলনা করুন

	দুর্বলভাবে তত্ত্বাবধানে থাকা BERT-ভিত্তিক শ্রেণীকরণ	স্ব-তত্ত্বাবধানে BERT-ভিত্তিক শ্রেণীকরণ
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017–2020	2019
প্রবর্তক	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)
ধরন	Weakly supervised fine-tuning of pre-trained language model	Pretrain-then-fine-tune transformer model
মৌলিক উৎস	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, 4171–4186. Association for Computational Linguistics.
অপর নাম	WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning	BERT fine-tuning for classification, BERT text classifier, self-supervised transformer classification, masked LM pretraining with classification head
সম্পর্কিত	6	0
সারসংক্ষেপ	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.	Self-supervised BERT-based classification uses Google's Bidirectional Encoder Representations from Transformers (BERT), pretrained on massive unlabelled text via masked-language modelling, and fine-tunes it on labelled examples to assign text into categories. It consistently achieves state-of-the-art accuracy on sentiment analysis, topic classification, intent detection, and similar NLP tasks even with limited labelled data.