Randomly masking 10% of input tokens during fine-tuning yields small F1 improvements on code-switched language identification and sentiment analysis across three transformer models.
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Token Masking Improves Transformer-Based Text Classification
Randomly masking 10% of input tokens during fine-tuning yields small F1 improvements on code-switched language identification and sentiment analysis across three transformer models.