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Found 4 papers, 4 papers with code
An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs
We apply our sparse accelerator on widely-used Transformer-based language models including Bert-Mini, DistilBERT, Bert-Base, and BERT-Large.
Fast DistilBERT on CPUs
In this work, we propose a new pipeline for creating and running Fast Transformer models on CPUs, utilizing hardware-aware pruning, knowledge distillation, quantization, and our own Transformer inference runtime engine with optimized kernels for sparse and quantized operators.
Prune Once for All: Sparse Pre-Trained Language Models
We show how the compressed sparse pre-trained models we trained transfer their knowledge to five different downstream natural language tasks with minimal accuracy loss.
Ranked #2 on
Natural Language Inference
on MultiNLI Dev
Q8BERT: Quantized 8Bit BERT
Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks.
Ranked #13 on
Semantic Textual Similarity
on STS Benchmark
