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[examples/seq2seq] more README improvements (#5274)

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Sam Shleifer
2020-06-25 10:13:01 -04:00
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@@ -125,10 +125,17 @@ Here is an example command, but you can do whatever you want. Hopefully this wil
--max_target_length=60 --val_max_target_length=60 --test_max_target_length=100 --max_target_length=60 --val_max_target_length=60 --test_max_target_length=100
``` ```
Results can be viewed [here](https://app.wandb.ai/sshleifer/hf_xsum?workspace=user-) You can see your wandb logs [here](https://app.wandb.ai/sshleifer/hf_xsum?workspace=user-)
### DistilBART
### Distilbart For the CNN/DailyMail dataset, (relatively longer, more extractive summaries), we found a simple technique that works:
you just copy alternating layers from `bart-large-cnn` and finetune more on the same data.
For the XSUM dataset, that didn’t work as well so we used that same initialization strategy followed by a combination of Distillbert’s ce_loss and the hidden states MSE loss used in the tinybert paper.
You can see the performance tradeoffs of model sizes [here](https://docs.google.com/spreadsheets/d/1EkhDMwVO02m8jCD1cG3RoFPLicpcL1GQHTQjfvDYgIM/edit#gid=0).
and more granular timing results [here](https://docs.google.com/spreadsheets/d/1EkhDMwVO02m8jCD1cG3RoFPLicpcL1GQHTQjfvDYgIM/edit#gid=1753259047&range=B2:I23).
#### No Teacher Distillation #### No Teacher Distillation
To run the simpler distilbart-cnn style distillation all you need is data, a GPU, and a properly initialized student. To run the simpler distilbart-cnn style distillation all you need is data, a GPU, and a properly initialized student.