KerasBERT: Modeling the Keras Language

Shorten, Connor; Khoshgoftaar, Taghi M.

doi:10.1109/icmla52953.2021.00041

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…When using BERT-base-uncased 1 as the pretrained model, we use the Keras-BERT (Shorten and Khoshgoftaar, 2021) module to implement the Tokenizer and import the model.…”

Section: Implementation Detailsmentioning

confidence: 99%

Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks

Li¹,

Zhou²

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This paper introduces the related work and the results of Team Sapphire's system for SemEval-2022 Task 4: Patronizing and Condescending Language Detection. We only participated in subtask 1. The task goal is to judge whether a news text contains PCL. This task can be considered as a task of binary classification of news texts. In this binary classification task, the BERT-base model is adopted as the pre-trained model used to represent textual information in vector form and encode it. Capsule networks is adopted to extract features from the encoded vectors. The official evaluation metric for subtask 1 is the F1 score over the positive class. Finally, our system's submitted prediction results on test set achieved the score of 0.5187.

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“…When using BERT-base-uncased 1 as the pretrained model, we use the Keras-BERT (Shorten and Khoshgoftaar, 2021) module to implement the Tokenizer and import the model.…”

Section: Implementation Detailsmentioning

confidence: 99%

Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks

Li¹,

Zhou²

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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“…The layers used and their function are described in the Keras API [11] . Rescaling layer: Preprocessing layer which rescales input values to a new range.…”

Section: Introductionmentioning

confidence: 99%