Binary Sentiment Classification With Distilled Transformer

via “binary-sentiment-classification-with-distilled-transformer”

text-classification model by undefined. 32,57,232 downloads.

Unique: Uses knowledge distillation from BERT to achieve 40% parameter reduction and 60% inference speedup while maintaining 97% of original BERT performance on SST-2, enabling deployment on resource-constrained environments where full BERT is infeasible. Fine-tuned specifically on SST-2's sentence-level annotations rather than document-level reviews, making it optimized for shorter text spans.

vs others: Faster and lighter than full BERT-base (110M vs 67M parameters) with better accuracy than rule-based or bag-of-words approaches, but less flexible than larger models like RoBERTa or DeBERTa for domain-specific fine-tuning due to smaller capacity.