metadata
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-summary-synthsumm-16384-v2
This was fine-tuned on a synthetic dataset of curated long-context text and GPT-3.5-turbo-1106 summaries spanning several domains, including "random" long-context examples from redpajama, the pile, etc. Try it in the gradio demo
Model description
This model is a fine-tuned version of 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