Sammenlign metoder

	Finjusteret BERT-baseret klassifikation	BERT-baseret klassifikation
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2019	2019
Ophavsperson	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Type	Pre-trained transformer fine-tuned for classification	Pre-trained language model with fine-tuning
Oprindelig kilde	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.	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.
Aliasser	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Relaterede	5	4
Resumé	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.	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.