---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---

# XtremeDistil-Transformers for Distilling Massive Neural Networks

XtremeDistil is a distilled task-agnostic transformer model leveraging multi-task distillation techniques from the paper "[XtremeDistil: Multi-stage Distillation for Massive Multilingual Models](https://www.aclweb.org/anthology/2020.acl-main.202.pdf)" and "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)" with the following "[Github code](https://github.com/microsoft/xtreme-distil-transformers)".

This l6-h384 checkpoint with **6** layers, **384** hidden size, **12** attention heads corresponds to **22 million** parameters with **5.3x** speedup over BERT-base.

The following table shows the results on GLUE dev set and SQuAD-v2.

| Models         | #Params | Speedup | MNLI | QNLI | QQP  | RTE  | SST  | MRPC | SQUAD2 | Avg   |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT        | 109    | 1x       | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 76.8   | 84.8 |
| DistilBERT  | 66     | 2x       | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 70.7   | 81.3 |
| TinyBERT    | 66     | 2x       | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 73.1   | 84.3 |
| MiniLM      | 66     | 2x       | 84.0   | 91.0   | 91.0   | 71.5 | 92.0   | 88.4 | 76.4   | 84.9  |
| MiniLM      | 22     | 5.3x     | 82.8 | 90.3 | 90.6 | 68.9 | 91.3 | 86.6 | 72.9   | 83.3 |
| XtremeDistil   | 22     | 5.3x     | 85.4 | 90.3 | 91.0   | 80.9 | 92.3 | 90.0   | 76.6   | 86.6 |

If you use this checkpoint in your work, please cite:

``` latex
@inproceedings{mukherjee-hassan-awadallah-2020-xtremedistil,
    title = "{X}treme{D}istil: Multi-stage Distillation for Massive Multilingual Models",
    author = "Mukherjee, Subhabrata  and
      Hassan Awadallah, Ahmed",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.202",
    doi = "10.18653/v1/2020.acl-main.202",
    pages = "2221--2234",
}
```

``` latex
@misc{wang2020minilm,
    title={MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers},
    author={Wenhui Wang and Furu Wei and Li Dong and Hangbo Bao and Nan Yang and Ming Zhou},
    year={2020},
    eprint={2002.10957},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```