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Efficient Mlm M0.15
E
Efficient Mlm M0.15
Developed by princeton-nlp
This model investigates the effectiveness of masking 15% of content in masked language modeling, employing a pre-layer normalization approach.
Downloads 116
Release Time : 4/22/2022
Model Overview
This model focuses on the masked language modeling task, exploring the impact of masking ratios on model performance, and adopts a pre-layer normalization architecture.
Model Features
Pre-layer normalization
Employs pre-layer normalization, an uncommon architectural choice that may affect model training stability.
Masking ratio research
Specifically studies the effectiveness of a 15% masking ratio in masked language modeling.
Model Capabilities
Masked language modeling
Text representation learning
Use Cases
Natural language processing research
Masking strategy research
Used to study the impact of different masking ratios on language model performance
Provides specific analysis of the effects of a 15% masking ratio
Text representation learning
Downstream task pre-training
Can serve as a pre-training model for other NLP tasks
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