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---
license: bsd-3-clause
base_model: pszemraj/pegasus-x-large-book-summary
tags:
- generated_from_trainer
- synthsumm
metrics:
- rouge
datasets:
- pszemraj/synthsumm
pipeline_tag: summarization
language:
- en
---
# pegasus-x-large-book_synthsumm
Fine-tuned on a synthetic dataset of curated long-context text and `GPT-3.5-turbo-1106` summaries spanning multiple domains + "random" long-context examples from pretraining datasets
Try it: [gradio demo](https://huggingface.co/spaces/pszemraj/document-summarization) | [example outputs .md](evals-outputs/GAUNTLET.md) (gauntlet) | code for free [HF inference api](https://gist.github.com/pszemraj/08f527380ed00ef2f2169e220341c489)
## Usage
It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If interested, you can also use the `textsum` [util repo](https://github.com/pszemraj/textsum) to have most of this abstracted out for you:
```bash
pip install -U textsum
```
```python
from textsum.summarize import Summarizer
model_name = "pszemraj/pegasus-x-large-book_synthsumm"
summarizer = Summarizer(model_name) # GPU auto-detected
text = "put the text you don't want to read here"
summary = summarizer.summarize_string(text)
print(summary)
```
## Details
This model is a fine-tuned version of [pszemraj/pegasus-x-large-book-summary](https://huggingface.co/pszemraj/pegasus-x-large-book-summary) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5481
- Rouge1: 48.141
- Rouge2: 19.1137
- Rougel: 33.647
- Rougelsum: 42.1211
- Gen Len: 73.9846
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 5309
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.7369 | 0.38 | 125 | 1.7140 | 43.0265 | 15.8613 | 30.5774 | 38.2507 | 77.0462 |
| 1.7736 | 0.77 | 250 | 1.6361 | 43.0209 | 15.2384 | 29.7678 | 37.4955 | 67.6 |
| 1.4251 | 1.15 | 375 | 1.5931 | 46.2138 | 17.5559 | 33.0091 | 41.0385 | 74.1077 |
| 1.2706 | 1.54 | 500 | 1.5635 | 44.6382 | 16.5917 | 30.7551 | 39.8466 | 71.7231 |
| 1.4844 | 1.92 | 625 | 1.5481 | 48.141 | 19.1137 | 33.647 | 42.1211 | 73.9846 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0 |