DistilBERT with a second step of distillation
Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
- Student:
distilbert-base-uncased - Teacher:
lewtun/bert-base-uncased-finetuned-squad-v1
Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the datasets library as follows:
from datasets import load_dataset
squad = load_dataset('squad')
Training procedure
Eval results
| Exact Match | F1 | |
|---|---|---|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the squad metric from datasets.
BibTeX entry and citation info
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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