Add to ORKGRecently, the development of pre-trained language models has brought natural language processing (NLP) tasks to the new state-of-the-art. In this paper we explore the efficiency of various pre-trained language models. We pre-train a list of transformer-based models with the same amount of text and the same training steps. The experimental results shows that the most improvement upon the origin BERT is adding the RNN-layer to capture more contextual information for short text understanding.Comment: working in progres
This paper reports on the evaluation of Deep Learning (DL) transformer architecture models for Named...
Transformer-based masked language models trained on general corpora, such as BERT and RoBERTa, have ...
These improvements open many possibilities in solving Natural Language Processing downstream tasks. ...
Since the first bidirectional deep learn- ing model for natural language understanding, BERT, emerge...
Transfer learning is to apply knowledge or patterns learned in a particular field or task to differe...
Natural language processing (NLP) techniques had significantly improved by introducing pre-trained l...
Natural language processing (NLP) techniques had significantly improved by introducing pre-trained l...
Natural language processing (NLP) techniques had significantly improved by introducing pre-trained l...
In the natural language processing (NLP) literature, neural networks are becoming increasingly deepe...
In transfer learning, two major activities, i.e., pretraining and fine-tuning, are carried out to pe...
Thesis (Ph.D.)--University of Washington, 2022A robust language processing machine should be able to...
GPT-2 and BERT demonstrate the effectiveness of using pre-trained language models (LMs) on various n...
Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models ...
Pre-trained language models have been dominating the field of natural language processing in recent ...
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on...
This paper reports on the evaluation of Deep Learning (DL) transformer architecture models for Named...
Transformer-based masked language models trained on general corpora, such as BERT and RoBERTa, have ...
These improvements open many possibilities in solving Natural Language Processing downstream tasks. ...
Since the first bidirectional deep learn- ing model for natural language understanding, BERT, emerge...
Transfer learning is to apply knowledge or patterns learned in a particular field or task to differe...
Natural language processing (NLP) techniques had significantly improved by introducing pre-trained l...
Natural language processing (NLP) techniques had significantly improved by introducing pre-trained l...
Natural language processing (NLP) techniques had significantly improved by introducing pre-trained l...
In the natural language processing (NLP) literature, neural networks are becoming increasingly deepe...
In transfer learning, two major activities, i.e., pretraining and fine-tuning, are carried out to pe...
Thesis (Ph.D.)--University of Washington, 2022A robust language processing machine should be able to...
GPT-2 and BERT demonstrate the effectiveness of using pre-trained language models (LMs) on various n...
Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models ...
Pre-trained language models have been dominating the field of natural language processing in recent ...
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on...
This paper reports on the evaluation of Deep Learning (DL) transformer architecture models for Named...
Transformer-based masked language models trained on general corpora, such as BERT and RoBERTa, have ...
These improvements open many possibilities in solving Natural Language Processing downstream tasks. ...