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Migrate model card from transformers-repo

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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/valhalla/distilbart-mnli-12-3/README.md

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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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+
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+ # DistilBart-MNLI
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+
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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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+
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+ We just copy alternating layers from `bart-large-mnli` and finetune more on the same data.
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+
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+
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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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+
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+
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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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+
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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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+
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ You can find the logs of these trained models in this [wandb project](https://wandb.ai/psuraj/distilbart-mnli).