Results 41 to 50 of about 10,485,189 (358)
Neural Language Models for Nineteenth-Century English
Journal of Open Humanities Data, 2021 We present four types of neural language models trained on a large historical dataset of books in English, published between 1760 and 1900, and comprised of ≈5.1 billion tokens.
Kasra Hosseini +3 more
doaj +1 more source
Table Search Using a Deep Contextualized Language Model
, 2020 Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks. Benefiting from multiple pretraining tasks and large scale training corpora, pretrained models can capture complex ...
Auer Sören +5 more
core +1 more source
I-BERT: Integer-only BERT Quantization
, 2021 Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive efficient inference at the edge, and even at the data center.
Kim, Sehoon +4 more
openaire +2 more sources
Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction [PDF]
npj Digital Medicine, 2020 Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption ...
L. Rasmy +4 more
semanticscholar +1 more source
Look at the First Sentence: Position Bias in Question Answering
, 2020 Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution
Kang, Jaewoo +4 more
core +1 more source
Patient Knowledge Distillation for BERT Model Compression [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 Pre-trained language models such as BERT have proven to be highly effective for natural language processing (NLP) tasks. However, the high demand for computing resources in training such models hinders their application in practice. In order to alleviate
S. Sun, Yu Cheng, Zhe Gan, Jingjing Liu
semanticscholar +1 more source
MaterialBERT for natural language processing of materials science texts
Science and Technology of Advanced Materials: Methods, 2022 A BERT (Bidirectional Encoder Representations from Transformers) model, which we named “MaterialBERT”, has been generated using scientific papers in wide area of material science as a corpus.
Michiko Yoshitake +3 more
doaj +1 more source
Sentiment Analysis of Twitter's Opinion on The Russia and Ukraine War Using Bert
Jurnal Riset Informatika, 2022 News about the war that took place between Russia and Ukraine can not be denied affecting various aspects of life in the world. This affects the writings of every citizen of the world on various social media platforms, one of which is Twitter.
Muhammad Fahmi Julianto +2 more
doaj +1 more source
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets [PDF]
BioNLP@ACL, 2019 Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the ...
Yifan Peng, Shankai Yan, Zhiyong Lu
semanticscholar +1 more source
BMJ Open Diabetes Research & Care, 2019 Objective While extensive research revealed that interleukin (IL)-1β contributes to insulin resistance (IR) development, the role of IL-1α in obesity and IR was scarcely studied.
Tal Almog +13 more
doaj +1 more source