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| BERT 기반 미세조정 분류× | BERT 기반 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2019 | 2019 |
| 창시자≠ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI) | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language) |
| 유형≠ | Pre-trained transformer fine-tuned for classification | Pre-trained language model with fine-tuning |
| 원전≠ | 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. DOI ↗ | 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. DOI ↗ |
| 별칭 | BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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