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---
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned_xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 34.0559
---
<!-- 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. -->
# t5-small-finetuned_xsum
This model is a fine-tuned version of [pki/t5-small-finetuned_xsum](https://huggingface.co/pki/t5-small-finetuned_xsum) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0479
- Rouge1: 34.0559
- Rouge2: 12.7506
- Rougel: 27.6762
- Rougelsum: 27.68
- Gen Len: 18.7924
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.1176 | 1.0 | 12753 | 2.0913 | 33.1548 | 11.8434 | 26.7805 | 26.7751 | 18.7805 |
| 2.1019 | 2.0 | 25506 | 2.0875 | 33.231 | 11.9329 | 26.8674 | 26.861 | 18.7992 |
| 2.1044 | 3.0 | 38259 | 2.0846 | 33.3643 | 11.9807 | 26.9817 | 26.9764 | 18.773 |
| 2.0874 | 4.0 | 51012 | 2.0832 | 33.3562 | 12.0681 | 27.0178 | 27.0189 | 18.7988 |
| 2.0791 | 5.0 | 63765 | 2.0803 | 33.38 | 12.081 | 27.0368 | 27.0344 | 18.7844 |
| 2.0894 | 6.0 | 76518 | 2.0787 | 33.2549 | 11.9662 | 26.8674 | 26.8669 | 18.7975 |
| 2.0802 | 7.0 | 89271 | 2.0777 | 33.3978 | 12.0828 | 27.0461 | 27.0443 | 18.7757 |
| 2.0719 | 8.0 | 102024 | 2.0743 | 33.4083 | 12.1141 | 27.0523 | 27.0457 | 18.7928 |
| 2.0782 | 9.0 | 114777 | 2.0748 | 33.3673 | 12.1637 | 27.0696 | 27.0663 | 18.7902 |
| 2.0736 | 10.0 | 127530 | 2.0713 | 33.5771 | 12.2219 | 27.1707 | 27.1706 | 18.7945 |
| 2.0816 | 11.0 | 140283 | 2.0703 | 33.5099 | 12.2069 | 27.1822 | 27.1835 | 18.8002 |
| 2.057 | 12.0 | 153036 | 2.0693 | 33.5853 | 12.2427 | 27.2096 | 27.2109 | 18.806 |
| 2.0584 | 13.0 | 165789 | 2.0676 | 33.4883 | 12.2674 | 27.1582 | 27.154 | 18.7857 |
| 2.0475 | 14.0 | 178542 | 2.0662 | 33.5529 | 12.2765 | 27.1897 | 27.1901 | 18.79 |
| 2.0426 | 15.0 | 191295 | 2.0643 | 33.6543 | 12.3545 | 27.2946 | 27.2928 | 18.8036 |
| 2.0373 | 16.0 | 204048 | 2.0648 | 33.6671 | 12.349 | 27.2649 | 27.2707 | 18.7905 |
| 2.0178 | 17.0 | 216801 | 2.0637 | 33.6794 | 12.4545 | 27.3015 | 27.3079 | 18.7948 |
| 2.0235 | 18.0 | 229554 | 2.0626 | 33.7635 | 12.423 | 27.3475 | 27.3446 | 18.7892 |
| 2.0296 | 19.0 | 242307 | 2.0622 | 33.7574 | 12.4651 | 27.3879 | 27.3882 | 18.8134 |
| 2.0319 | 20.0 | 255060 | 2.0595 | 33.9093 | 12.5389 | 27.5003 | 27.5001 | 18.7915 |
| 2.0208 | 21.0 | 267813 | 2.0583 | 33.7875 | 12.4912 | 27.4243 | 27.4332 | 18.7982 |
| 2.0151 | 22.0 | 280566 | 2.0581 | 33.8516 | 12.4805 | 27.46 | 27.4647 | 18.816 |
| 2.0188 | 23.0 | 293319 | 2.0575 | 33.7744 | 12.4548 | 27.381 | 27.382 | 18.802 |
| 2.0087 | 24.0 | 306072 | 2.0579 | 33.8953 | 12.4984 | 27.4675 | 27.4727 | 18.7819 |
| 2.0186 | 25.0 | 318825 | 2.0557 | 33.7766 | 12.4414 | 27.4025 | 27.4024 | 18.8005 |
| 2.0051 | 26.0 | 331578 | 2.0555 | 33.8973 | 12.5796 | 27.5338 | 27.5339 | 18.8153 |
| 2.0024 | 27.0 | 344331 | 2.0557 | 33.8709 | 12.5116 | 27.4684 | 27.4664 | 18.7911 |
| 1.9947 | 28.0 | 357084 | 2.0545 | 33.8499 | 12.5242 | 27.4677 | 27.4716 | 18.8025 |
| 1.9931 | 29.0 | 369837 | 2.0545 | 33.7957 | 12.5272 | 27.4129 | 27.4174 | 18.8 |
| 1.9826 | 30.0 | 382590 | 2.0548 | 33.9723 | 12.6665 | 27.5598 | 27.5662 | 18.7958 |
| 1.999 | 31.0 | 395343 | 2.0522 | 33.9702 | 12.6435 | 27.5788 | 27.579 | 18.795 |
| 1.9872 | 32.0 | 408096 | 2.0525 | 33.9546 | 12.638 | 27.5985 | 27.5949 | 18.7976 |
| 1.991 | 33.0 | 420849 | 2.0520 | 33.9792 | 12.6073 | 27.5686 | 27.5707 | 18.8056 |
| 2.0044 | 34.0 | 433602 | 2.0504 | 34.0736 | 12.6511 | 27.647 | 27.6472 | 18.8093 |
| 1.9972 | 35.0 | 446355 | 2.0513 | 34.0506 | 12.711 | 27.6533 | 27.6537 | 18.7984 |
| 1.9901 | 36.0 | 459108 | 2.0504 | 33.9991 | 12.638 | 27.626 | 27.6272 | 18.7996 |
| 1.9742 | 37.0 | 471861 | 2.0507 | 33.9357 | 12.6636 | 27.5673 | 27.5716 | 18.8064 |
| 1.984 | 38.0 | 484614 | 2.0502 | 33.9476 | 12.6589 | 27.58 | 27.5813 | 18.8037 |
| 1.9864 | 39.0 | 497367 | 2.0499 | 34.0733 | 12.7198 | 27.6926 | 27.6992 | 18.8061 |
| 1.9734 | 40.0 | 510120 | 2.0492 | 33.9483 | 12.6486 | 27.5571 | 27.5598 | 18.8033 |
| 1.9895 | 41.0 | 522873 | 2.0490 | 33.9753 | 12.684 | 27.6058 | 27.6086 | 18.8011 |
| 1.964 | 42.0 | 535626 | 2.0487 | 33.9528 | 12.6376 | 27.576 | 27.5824 | 18.7919 |
| 1.9849 | 43.0 | 548379 | 2.0487 | 33.9868 | 12.6936 | 27.6116 | 27.6158 | 18.7966 |
| 1.9798 | 44.0 | 561132 | 2.0491 | 34.0379 | 12.7161 | 27.6227 | 27.6315 | 18.7889 |
| 1.9837 | 45.0 | 573885 | 2.0473 | 34.0046 | 12.6559 | 27.5931 | 27.5988 | 18.7996 |
| 1.9556 | 46.0 | 586638 | 2.0483 | 34.0378 | 12.712 | 27.6346 | 27.6446 | 18.7942 |
| 1.9844 | 47.0 | 599391 | 2.0479 | 34.0301 | 12.7121 | 27.6492 | 27.6554 | 18.7999 |
| 1.9869 | 48.0 | 612144 | 2.0474 | 34.0463 | 12.7151 | 27.6542 | 27.6604 | 18.7919 |
| 1.9851 | 49.0 | 624897 | 2.0476 | 34.0549 | 12.7384 | 27.6542 | 27.6555 | 18.7924 |
| 1.9912 | 50.0 | 637650 | 2.0479 | 34.0559 | 12.7506 | 27.6762 | 27.68 | 18.7924 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.10.1
- Datasets 1.14.0
- Tokenizers 0.10.3
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