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--- |
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license: bsd-3-clause |
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base_model: pszemraj/pegasus-x-large-book-summary |
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tags: |
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- generated_from_trainer |
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- synthsumm |
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metrics: |
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- rouge |
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datasets: |
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- pszemraj/synthsumm |
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pipeline_tag: summarization |
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language: |
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- en |
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--- |
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# pegasus-x-large-book_synthsumm |
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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 |
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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) |
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## Usage |
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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: |
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```bash |
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pip install -U textsum |
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``` |
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```python |
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from textsum.summarize import Summarizer |
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model_name = "pszemraj/pegasus-x-large-book_synthsumm" |
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summarizer = Summarizer(model_name) # GPU auto-detected |
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text = "put the text you don't want to read here" |
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summary = summarizer.summarize_string(text) |
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print(summary) |
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``` |
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## Details |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5481 |
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- Rouge1: 48.141 |
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- Rouge2: 19.1137 |
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- Rougel: 33.647 |
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- Rougelsum: 42.1211 |
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- Gen Len: 73.9846 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 5309 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: inverse_sqrt |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.7369 | 0.38 | 125 | 1.7140 | 43.0265 | 15.8613 | 30.5774 | 38.2507 | 77.0462 | |
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| 1.7736 | 0.77 | 250 | 1.6361 | 43.0209 | 15.2384 | 29.7678 | 37.4955 | 67.6 | |
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| 1.4251 | 1.15 | 375 | 1.5931 | 46.2138 | 17.5559 | 33.0091 | 41.0385 | 74.1077 | |
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| 1.2706 | 1.54 | 500 | 1.5635 | 44.6382 | 16.5917 | 30.7551 | 39.8466 | 71.7231 | |
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| 1.4844 | 1.92 | 625 | 1.5481 | 48.141 | 19.1137 | 33.647 | 42.1211 | 73.9846 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.0 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
