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- RoBERTa: A Robustly Optimized BERT Pretraining Approach
We evaluate a number of design decisions when pretraining BERT models and propose an improved recipe that achieves state-of-the-art results on many natural language understanding tasks
- ROBERTA: A ROBUSTLY OPTIMIZED BERT PRE TRAINING APPROACH
We present a replication study of BERT pretraining (Devlin et al , 2019), which includes a careful evaluation of the effects of hyperparameter tuning and training set size We find that BERT was sig-nificantly undertrained and propose an improved training recipe, which we call RoBERTa, that can match or exceed the performance of all of the post-BERT methods Our modifications are simple, they
- D BERT : D BERT D ENTANGLED ATTENTION - OpenReview
ABSTRACT Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks In this pa-per we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques The first is the disentangled attention mechanism, where
- RoBERTa vs BERT for intent classification - OpenReview
RoBERTa [18]: Robustly Optimized BERT-Pretraining Approach proposed in Liu et al is an extension to the original BERT model Like BERT, RoBERTa is a natural language process-ing model based on Transformer neural networks
- S BERT: INCORPORATING LANGUAGE STRUCTURES INTO PRE . . . - OpenReview
ABSTRACT Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering Inspired by the linearization exploration
- A ROBUSTLY AND EFFECTIVELY OPTIMIZED PRE APPROACH FOR MASKED . . .
ABSTRACT Recently, Masked Image Modeling (MIM) has increasingly reshaped the status quo of self-supervised visual pre-training This paper does not describe a novel MIM framework, but to unravel several fundamental ingredients to robustly and effectively pre-train a Masked AutoEncoder (MAE) with improved downstream performance as a byproduct We highlight the great significance for the whole
- MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining
RoBERTa (“Robustly optimized BERT approach”) is the most influential work in this regard [37] In this study, they kept the exact BERT architecture but changed the training recipe by removing the next sentence prediction objective, training for longer on much larger datasets, and changing the batch size, among other things
- MosaicBERT: How to Train BERT with a Lunch Money Budget
RoBERTa (“Robustly optimized BERT approach”) is the most influential work in this regard (Liu et al , 2019) In this study, they kept the exact BERT architecture but changed the training recipe by removing the next sentence predic-tion objective, training for longer on much larger datasets, and changing the batch size, among other things
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