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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
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Transfer fine-tuned BERT models by paraphrases

Authors: Yuki Arase; Junichi Tsujii;

Transfer fine-tuned BERT models by paraphrases

Abstract

Transfer fine-tuned BERT models by phrasal paraphrases. transferFT_bert-base-uncased.pkl bases on the bert-base-uncased model transferFT_bert-large-uncased.pkl bases on the bert-large-uncased model For usage, please refer to our GitHub page. https://github.com/yukiar/TransferFT For details of these models, please refer to our paper. Yuki Arase and Junichi Tsujii. 2019. Transfer Fine-Tuning: A BERT Case Study. in Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP 2019). https://arxiv.org/abs/1909.00931

When you have publications using our models, please cite our EMNLP2019 paper.

Related Organizations
Keywords

sentence representation, paraphrase, pre-trained model, NLP, BERT

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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