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---
license: mit
base_model: arthurmluz/ptt5-xlsumm-30epochs
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ptt5-xlsumm-temario
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. -->
# ptt5-xlsumm-temario
This model is a fine-tuned version of [arthurmluz/ptt5-xlsumm-30epochs](https://huggingface.co/arthurmluz/ptt5-xlsumm-30epochs) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4610
- Rouge1: 0.0891
- Rouge2: 0.0571
- Rougel: 0.0781
- Rougelsum: 0.0845
- Gen Len: 19.0
## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 88 | 2.7016 | 0.0862 | 0.0326 | 0.0681 | 0.0805 | 19.0 |
| No log | 2.0 | 176 | 2.6413 | 0.0879 | 0.0389 | 0.0701 | 0.0828 | 19.0 |
| 2.9296 | 3.0 | 264 | 2.5893 | 0.0881 | 0.0438 | 0.0707 | 0.0827 | 19.0 |
| 2.9296 | 4.0 | 352 | 2.5650 | 0.0923 | 0.0479 | 0.0748 | 0.0871 | 19.0 |
| 2.646 | 5.0 | 440 | 2.5429 | 0.0885 | 0.0469 | 0.0732 | 0.0834 | 19.0 |
| 2.646 | 6.0 | 528 | 2.5247 | 0.088 | 0.0503 | 0.0739 | 0.0831 | 19.0 |
| 2.5072 | 7.0 | 616 | 2.5108 | 0.0891 | 0.0534 | 0.0769 | 0.0851 | 19.0 |
| 2.5072 | 8.0 | 704 | 2.5039 | 0.0884 | 0.0547 | 0.0764 | 0.0848 | 19.0 |
| 2.5072 | 9.0 | 792 | 2.4948 | 0.0864 | 0.0536 | 0.0751 | 0.083 | 19.0 |
| 2.4128 | 10.0 | 880 | 2.4836 | 0.0869 | 0.0546 | 0.076 | 0.0839 | 19.0 |
| 2.4128 | 11.0 | 968 | 2.4813 | 0.0866 | 0.0543 | 0.0764 | 0.0832 | 19.0 |
| 2.356 | 12.0 | 1056 | 2.4768 | 0.0864 | 0.0533 | 0.076 | 0.0828 | 19.0 |
| 2.356 | 13.0 | 1144 | 2.4728 | 0.0872 | 0.0556 | 0.0775 | 0.0838 | 19.0 |
| 2.2815 | 14.0 | 1232 | 2.4666 | 0.0877 | 0.0557 | 0.0774 | 0.0841 | 19.0 |
| 2.2815 | 15.0 | 1320 | 2.4667 | 0.0866 | 0.0552 | 0.0764 | 0.0829 | 19.0 |
| 2.2106 | 16.0 | 1408 | 2.4680 | 0.0869 | 0.0553 | 0.0772 | 0.0824 | 19.0 |
| 2.2106 | 17.0 | 1496 | 2.4647 | 0.0867 | 0.0553 | 0.0771 | 0.0828 | 19.0 |
| 2.2106 | 18.0 | 1584 | 2.4597 | 0.0875 | 0.0561 | 0.0777 | 0.0837 | 19.0 |
| 2.1809 | 19.0 | 1672 | 2.4601 | 0.0873 | 0.0557 | 0.0773 | 0.0833 | 19.0 |
| 2.1809 | 20.0 | 1760 | 2.4596 | 0.0873 | 0.0561 | 0.0773 | 0.0835 | 19.0 |
| 2.1541 | 21.0 | 1848 | 2.4592 | 0.0875 | 0.0561 | 0.0777 | 0.0837 | 19.0 |
| 2.1541 | 22.0 | 1936 | 2.4620 | 0.0869 | 0.0551 | 0.0768 | 0.0828 | 19.0 |
| 2.1442 | 23.0 | 2024 | 2.4621 | 0.0869 | 0.0551 | 0.0768 | 0.0828 | 19.0 |
| 2.1442 | 24.0 | 2112 | 2.4619 | 0.0868 | 0.0553 | 0.0768 | 0.0828 | 19.0 |
| 2.1071 | 25.0 | 2200 | 2.4613 | 0.0868 | 0.0553 | 0.0768 | 0.0828 | 19.0 |
| 2.1071 | 26.0 | 2288 | 2.4618 | 0.0873 | 0.0557 | 0.0768 | 0.0828 | 19.0 |
| 2.1071 | 27.0 | 2376 | 2.4607 | 0.0892 | 0.0575 | 0.0782 | 0.0847 | 19.0 |
| 2.08 | 28.0 | 2464 | 2.4606 | 0.0874 | 0.056 | 0.0769 | 0.083 | 19.0 |
| 2.08 | 29.0 | 2552 | 2.4616 | 0.0891 | 0.0571 | 0.0781 | 0.0845 | 19.0 |
| 2.1013 | 30.0 | 2640 | 2.4610 | 0.0891 | 0.0571 | 0.0781 | 0.0845 | 19.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
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