59 lines
2.3 KiB
Markdown
59 lines
2.3 KiB
Markdown
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---
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datasets:
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- mnli
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tags:
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- distilbart
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- distilbart-mnli
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pipeline_tag: zero-shot-classification
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---
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# DistilBart-MNLI
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distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, [here](https://github.com/huggingface/transformers/tree/master/examples/seq2seq#distilbart).
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We just copy alternating layers from `bart-large-mnli` and finetune more on the same data.
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| | matched acc | mismatched acc |
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| ------------------------------------------------------------------------------------ | ----------- | -------------- |
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| [bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) (baseline, 12-12) | 89.9 | 90.01 |
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| [distilbart-mnli-12-1](https://huggingface.co/valhalla/distilbart-mnli-12-1) | 87.08 | 87.5 |
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| [distilbart-mnli-12-3](https://huggingface.co/valhalla/distilbart-mnli-12-3) | 88.1 | 88.19 |
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| [distilbart-mnli-12-6](https://huggingface.co/valhalla/distilbart-mnli-12-6) | 89.19 | 89.01 |
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| [distilbart-mnli-12-9](https://huggingface.co/valhalla/distilbart-mnli-12-9) | 89.56 | 89.52 |
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This is a very simple and effective technique, as we can see the performance drop is very little.
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Detailed performace trade-offs will be posted in this [sheet](https://docs.google.com/spreadsheets/d/1dQeUvAKpScLuhDV1afaPJRRAE55s2LpIzDVA5xfqxvk/edit?usp=sharing).
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## Fine-tuning
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If you want to train these models yourself, clone the [distillbart-mnli repo](https://github.com/patil-suraj/distillbart-mnli) and follow the steps below
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Clone and install transformers from source
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```bash
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git clone https://github.com/huggingface/transformers.git
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pip install -qqq -U ./transformers
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```
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Download MNLI data
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```bash
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python transformers/utils/download_glue_data.py --data_dir glue_data --tasks MNLI
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```
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Create student model
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```bash
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python create_student.py \
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--teacher_model_name_or_path facebook/bart-large-mnli \
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--student_encoder_layers 12 \
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--student_decoder_layers 6 \
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--save_path student-bart-mnli-12-6 \
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```
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Start fine-tuning
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```bash
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python run_glue.py args.json
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```
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You can find the logs of these trained models in this [wandb project](https://wandb.ai/psuraj/distilbart-mnli).
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