Papers › Efficient transfer learning for NLP with ELECTRA

Efficient transfer learning for NLP with ELECTRA

6 Apr 2021arXiv:2104.02756archive 2025-07-28

François Mercier

Clark et al. [2020] claims that the ELECTRA approach is highly efficient in NLP performances relative to computation budget. As such, this reproducibility study focus on this claim, summarized by the following question: Can we use ELECTRA to achieve close to SOTA performances for NLP in low-resource settings, in term of compute cost?

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Code

cccwam/rc2020_electra officialmentioned in papermentioned on GitHubpytorch report

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Transfer Learning

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Methods

Adam Attention Attention Dropout Dense Connections Dropout ELECTRA Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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