distilbart-mnli is the distilled version of bart-large-mnli created using the No Teacher Distillation technique proposed for BART summarisation by Huggingface, here.
We just copy alternating layers from
bart-large-mnli and finetune more on the same data.
|matched acc||mismatched acc|
|bart-large-mnli (baseline, 12-12)||89.9||90.01|
This is a very simple and effective technique, as we can see the performance drop is very little.
Detailed performace trade-offs will be posted in this sheet.
If you want to train these models yourself, clone the distillbart-mnli repo and follow the steps below
Clone and install transformers from source
git clone https://github.com/huggingface/transformers.git pip install -qqq -U ./transformers
Download MNLI data
python transformers/utils/download_glue_data.py --data_dir glue_data --tasks MNLI
Create student model
python create_student.py \ --teacher_model_name_or_path facebook/bart-large-mnli \ --student_encoder_layers 12 \ --student_decoder_layers 6 \ --save_path student-bart-mnli-12-6 \
python run_glue.py args.json
You can find the logs of these trained models in this wandb project.
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This model can be loaded on the Inference API on-demand.