AI/NLP12 BART: Denoising Sequence-to-Sequence Pre-training for NaturalLanguage Generation, Translation, and Comprehension - BART BART: A Robustly Optimized BERT Pretraining Approach Paper : https://arxiv.org/pdf/1910.13461.pdf Abstract BART a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by corrupting text with an arbitrary noising function learning a model to reconstruct the original text uses a standard Tranformer-based neural machine translation architecture which, despite its simpl.. 2022. 4. 1. RoBERTa: A Robustly Optimized BERT Pretraining Approach - RoBERTa RoBERTa: A Robustly Optimized BERT Pretraining Approach Paper : https://arxiv.org/pdf/1907.11692.pdf Code : https://github.com/pytorch/fairseq GitHub - pytorch/fairseq: Facebook AI Research Sequence-to-Sequence Toolkit written in Python. Facebook AI Research Sequence-to-Sequence Toolkit written in Python. - GitHub - pytorch/fairseq: Facebook AI Research Sequence-to-Sequence Toolkit written in Py.. 2022. 3. 27. Improving Language Understanding by Generative Pre-Training - GPT1 Improving Language Understanding by Generative Pre-Training Paper : Link Code : Link Description : GPT1 Abstract Motivation (Background of Study) Although large unlabeled text corpora are abundant, labeled data for learning these specific tasks is scarce, making it challenging for discriminatively trained models to perform adequately. Achievement (Research Result) We demonstrate that large gains.. 2022. 3. 11. 이전 1 2 다음
