peterszemraj@gmail.com
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README.md
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# checkpoints
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- eval_loss: 1.1193
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- eval_runtime: 6.6754
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- epoch: 3.0
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- step: 900
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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# checkpoints
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This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset for four total epochs.
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It achieves the following results on the evaluation set:
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- eval_loss: 1.1193
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- eval_runtime: 6.6754
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- epoch: 3.0
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- step: 900
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A 1-epoch checkpoint can be found at [pszemraj/pegasus-large-book-summary](https://huggingface.co/pszemraj/pegasus-large-book-summary), which is where the second training session started from.
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## Model description
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- After some initial tests, it was found that models trained on the [booksum](https://github.com/salesforce/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.
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- 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.
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## Intended uses & limitations
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- 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.
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- this was only trained on the dataset for an epoch but still provides reasonable results.
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## Training and evaluation data
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