Timing optimization via nest-loop pipelining considering code size

2008 ◽  
Vol 32 (7) ◽  
pp. 351-363 ◽  
Author(s):  
Qingfeng Zhuge ◽  
Chun Jason Xue ◽  
Meikang Qiu ◽  
Jingtong Hu ◽  
Edwin H.-M. Sha
Keyword(s):  
Code Size ◽  
Timing Optimization ◽  
Nest Loop ◽  
Loop Pipelining

scholarly journals Transformers-sklearn: a toolkit for medical language understanding with transformer-based models

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Feihong Yang ◽  
Xuwen Wang ◽  
Hetong Ma ◽  
Jiao Li
Keyword(s):  
Language Processing ◽  
Pearson Correlation ◽  
Fine Tuning ◽  
Entity Recognition ◽  
Training Dataset ◽  
Training Methods ◽  
Code Size ◽  
Model Framework ◽  
Language Understanding ◽  
Medical Language

Abstract Background Transformer is an attention-based architecture proven the state-of-the-art model in natural language processing (NLP). To reduce the difficulty of beginning to use transformer-based models in medical language understanding and expand the capability of the scikit-learn toolkit in deep learning, we proposed an easy to learn Python toolkit named transformers-sklearn. By wrapping the interfaces of transformers in only three functions (i.e., fit, score, and predict), transformers-sklearn combines the advantages of the transformers and scikit-learn toolkits. Methods In transformers-sklearn, three Python classes were implemented, namely, BERTologyClassifier for the classification task, BERTologyNERClassifier for the named entity recognition (NER) task, and BERTologyRegressor for the regression task. Each class contains three methods, i.e., fit for fine-tuning transformer-based models with the training dataset, score for evaluating the performance of the fine-tuned model, and predict for predicting the labels of the test dataset. transformers-sklearn is a user-friendly toolkit that (1) Is customizable via a few parameters (e.g., model_name_or_path and model_type), (2) Supports multilingual NLP tasks, and (3) Requires less coding. The input data format is automatically generated by transformers-sklearn with the annotated corpus. Newcomers only need to prepare the dataset. The model framework and training methods are predefined in transformers-sklearn. Results We collected four open-source medical language datasets, including TrialClassification for Chinese medical trial text multi label classification, BC5CDR for English biomedical text name entity recognition, DiabetesNER for Chinese diabetes entity recognition and BIOSSES for English biomedical sentence similarity estimation. In the four medical NLP tasks, the average code size of our script is 45 lines/task, which is one-sixth the size of transformers’ script. The experimental results show that transformers-sklearn based on pretrained BERT models achieved macro F1 scores of 0.8225, 0.8703 and 0.6908, respectively, on the TrialClassification, BC5CDR and DiabetesNER tasks and a Pearson correlation of 0.8260 on the BIOSSES task, which is consistent with the results of transformers. Conclusions The proposed toolkit could help newcomers address medical language understanding tasks using the scikit-learn coding style easily. The code and tutorials of transformers-sklearn are available at https://doi.org/10.5281/zenodo.4453803. In future, more medical language understanding tasks will be supported to improve the applications of transformers_sklearn.

Linking mango infestation by fruit flies to fruit maturity and fly pressure: A prerequisite to improve fruit fly damage management via harvest timing optimization

2021 ◽  
Vol 146 ◽  
pp. 105663
Author(s):  
Isabelle Grechi ◽  
Anne-Laure Preterre ◽  
Aude Caillat ◽  
Frédéric Chiroleu ◽  
Alain Ratnadass
Keyword(s):  
Fruit Fly ◽  
Fruit Flies ◽  
Timing Optimization ◽  
Fruit Maturity ◽  
Harvest Timing

ANGHABENCH: A Suite with One Million Compilable C Benchmarks for Code-Size Reduction

2021 ◽  
Author(s):  
Anderson Faustino da Silva ◽  
Bruno Conde Kind ◽  
Jose Wesley de Souza Magalhaes ◽  
Jeronimo Nunes Rocha ◽  
Breno Campos Ferreira Guimaraes ◽  
...  
Keyword(s):  
Size Reduction ◽  
Code Size

Survey of code-size reduction methods

2003 ◽  
Vol 35 (3) ◽  
pp. 223-267 ◽  
Author(s):  
Árpád Beszédes ◽  
Rudolf Ferenc ◽  
Tibor Gyimóthy ◽  
André Dolenc ◽  
Konsta Karsisto
Keyword(s):  
Size Reduction ◽  
Code Size ◽  
Reduction Methods
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