tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets:
- bookcorpus
- wikipedia
oBERT-3-upstream-pretrained-dense
This model is obtained with The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.
It corresponds to 3 layers from neuralmagic/oBERT-12-upstream-pretrained-dense, pretrained with knowledge distillation. This model is used as a starting point for downstream finetuning and pruning runs presented in the Table 3 - 3 Layers.
The model can also be used for finetuning on any downstream task, as a starting point instead of the three times larger bert-base-uncased model.
Finetuned and pruned versions of this model on the SQuADv1 downstream task, as described in the paper:
- 0%:
neuralmagic/oBERT-3-downstream-dense-squadv1 - 80% unstructured:
neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1 - 80% block-4:
neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1 - 90% unstructured:
neuralmagic/oBERT-3-downstream-pruned-unstructured-90-squadv1 - 90% block-4:
neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1
Training objective: masked language modeling (MLM) + knowledge distillation
Paper: https://arxiv.org/abs/2203.07259
Dataset: BookCorpus and English Wikipedia
Sparsity: 0%
Number of layers: 3
Code: https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT
If you find the model useful, please consider citing our work.
Citation info
@article{kurtic2022optimal,
title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
journal={arXiv preprint arXiv:2203.07259},
year={2022}
}