metadata
license: apache-2.0
language:
- en
Model weights for Parallel Roberta-Large model
We provide the weights for the parallel attention and feedforward design for Roberta-Large.
Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
|---|---|---|---|---|---|---|---|---|
| 89.3 | 91.7 | 94.3 | 96.2 | 64.0 | 91.0 | 90.4 | 80.1 |
If you use this work, please cite: Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design: https://arxiv.org/abs/2305.13297
@misc{sonkar2023investigating,
title={Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design},
author={Shashank Sonkar and Richard G. Baraniuk},
year={2023},
eprint={2305.13297},
archivePrefix={arXiv},
primaryClass={cs.CL}
}