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.
To use this model, use the following paf_modeling_roberta.py file.
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import RobertaTokenizer
from paf_modeling_roberta import RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('roberta-large')
model = RobertaModel.from_pretrained('luffycodes/parallel-roberta-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
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}
}