language:
- en
license: apache-2.0
tags:
- SEAD
datasets:
- glue
- mnli
Paper
SEAD: SIMPLE ENSEMBLE AND KNOWLEDGE DISTILLATION FRAMEWORK FOR NATURAL LANGUAGE UNDERSTANDING
Aurthors: Moyan Mei, Rohit Sroch
Abstract
With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving guidance from a large PLM. While there are many successful designs for learning knowledge from teachers, it remains unclear how students can learn better. Inspired by real university teaching processes, in this work we further explore knowledge distillation and propose a very simple yet effective framework, SEAD, to further improve task-specific generalization by utilizing multiple teachers. Our experiments show that SEAD leads to better performance compared to other popular KD methods [1] [2] [3] and achieves comparable or superior performance to its teacher model such as BERT [4] on total 13 tasks for the GLUE [5] and SuperGLUE [6] benchmarks.
Moyan Mei and Rohit Sroch. 2022. SEAD: Simple ensemble and knowledge distillation framework for natural language understanding. Lattice, THE MACHINE LEARNING JOURNAL by Association of Data Scientists, 3(1).
SEAD-L-6_H-384_A-12-mnli
This is a student model distilled from BERT base as teacher by using SEAD framework on mnli task. For weights initialization, we used microsoft/xtremedistil-l6-h384-uncased
All SEAD Checkpoints
Other Community Checkpoints: here
Intended uses & limitations
More information needed
Training hyperparameters
Please take a look at the training_args.bin file
$ import torch
$ hyperparameters = torch.load(os.path.join('training_args.bin'))
Evaluation results
| eval_m-accuracy | eval_m-runtime | eval_m-samples_per_second | eval_m-steps_per_second | eval_m-loss | eval_m-samples | eval_mm-accuracy | eval_mm-runtime | eval_mm-samples_per_second | eval_mm-steps_per_second | eval_mm-loss | eval_mm-samples |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.8495 | 6.5443 | 1499.776 | 46.911 | 0.4366 | 9815 | 0.8508 | 5.6975 | 1725.678 | 54.059 | 0.4252 | 9832 |
Framework versions
- Transformers >=4.8.0
- Pytorch >=1.6.0
- TensorFlow >=2.5.0
- Flax >=0.3.5
- Datasets >=1.10.2
- Tokenizers >=0.11.6
If you use these models, please cite the following paper:
```
@article{article,
author={Mei, Moyan and Sroch, Rohit},
title={SEAD: Simple Ensemble and Knowledge Distillation Framework for Natural Language Understanding},
volume={3},
number={1},
journal={Lattice, The Machine Learning Journal by Association of Data Scientists},
day={26},
year={2022},
month={Feb},
url = {www.adasci.org/journals/lattice-35309407/?volumes=true&open=621a3b18edc4364e8a96cb63}
}
```