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SciWING -- A Software Toolkit for Scientific Document Processing

2020-04-08 · Abhinav Ramesh Kashyap, Min-Yen Kan

We introduce SciWING, an open-source software toolkit which provides access to pre-trained models for scientific document processing tasks, inclusive of citation string parsing and logical structure recovery. SciWING enables researchers to rapidly experiment with different models by swapping and stacking different modules. It also enables them declare and run models from a configuration file. It enables researchers to perform production-ready transfer learning from general, pre-trained transformers (i.e., BERT, SciBERT etc), and aids development of end-user applications. It includes ready-to-use web and terminal-based applications and demonstrations (Available from http://sciwing.io).

📄 PDF Abstract BibTeX arXiv:2004.03807

Code (1)

abhinavkashyap/sciwing pytorch

Tasks

Transfer Learning

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Weight Decay 설명 없음
Residual Connection 설명 없음
Adam 설명 없음
Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Attention 설명 없음
Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

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