# pszemraj/pegasus-large-summary-explain

This model is a fine-tuned version of google/pegasus-large on the booksum dataset for four total epochs.

It achieves the following results on the evaluation set:

- eval_loss: 1.1193
- eval_runtime: 6.6754
- eval_samples_per_second: 27.714
- eval_steps_per_second: 1.798
- epoch: 3.0
- step: 900

A 1-epoch checkpoint can be found at pszemraj/pegasus-large-book-summary, which is where the second training session started from.

## Model description

- After some initial tests, it was found that models trained on the booksum dataset seem to inherit the summaries' SparkNotes-style explanations; so the user gets a shorter and easier-to-understand version of the text instead of
**just**more compact. - This quality (anecdotally) is favourable for learning/comprehension because summarization datasets that simply make the information more compact (* cough * arXiv) can be so dense that the overall time spent trying to
*comprehend*what it is saying can be the same as just reading the original material.

## Intended uses & limitations

- standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer than 1024 tokens may be excluded from the final summary/the model will be biased towards information presented first.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- learning_rate: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4

### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0

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