26 июл. 2019 г. · We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size.

Review: This paper presents a replication study of BERT pretraining and carefully measures the impact of many key hyperparameters and training data size. It ...

26 июл. 2019 г. · This work presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT, and uses a self-supervised loss.

26 июл. 2019 г. · We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size.

We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size.

26 июл. 2019 г. · In the rest of the paper, we evaluate our best. RoBERTa model on the three different bench- marks: GLUE, SQuaD and RACE. Specifically. 9Our ...

We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size.

RoBERTa is an extension of BERT with changes to the pretraining procedure. The modifications include: training the model longer, with bigger batches, ...

16 нояб. 2024 г. · RoBERTa is a replication study of BERT pretraining that focuses on the impact of various hyperparameters and training data sizes.

It is based on Google's BERT model released in 2018. It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective. XLM-RoBERTa · RoBERTa-PreLayerNorm · XLM-RoBERTa-XL