| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - rouge |
| | model-index: |
| | - name: pegasus-xsum_summarization |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # pegasus-xsum_summarization |
| | |
| | This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.7876 |
| | - Rouge1: 25.7148 |
| | - Rouge2: 10.9685 |
| | - Rougel: 21.6394 |
| | - Rougelsum: 22.3122 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 5.6e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 8 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
| | | 1.633 | 1.0 | 50 | 1.8839 | 24.9703 | 9.6517 | 19.9759 | 21.088 | |
| | | 1.3917 | 2.0 | 100 | 1.8565 | 24.4395 | 9.1755 | 19.5702 | 20.5489 | |
| | | 1.2576 | 3.0 | 150 | 1.8361 | 24.8266 | 10.2009 | 20.074 | 21.382 | |
| | | 1.1191 | 4.0 | 200 | 1.8226 | 25.9635 | 11.7704 | 21.7787 | 22.365 | |
| | | 1.1138 | 5.0 | 250 | 1.8239 | 26.7874 | 12.2832 | 22.7792 | 23.4501 | |
| | | 1.0338 | 6.0 | 300 | 1.8094 | 26.3543 | 12.0501 | 22.3172 | 23.1194 | |
| | | 1.0084 | 7.0 | 350 | 1.7923 | 25.5686 | 11.0213 | 21.5288 | 21.8892 | |
| | | 1.0098 | 8.0 | 400 | 1.7876 | 25.7148 | 10.9685 | 21.6394 | 22.3122 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.20.1 |
| | - Pytorch 1.12.0 |
| | - Datasets 2.3.2 |
| | - Tokenizers 0.12.1 |
| | |
