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---
tags:
- summarization
- mT5
language:
- zh
widget:
- text: "專家稱維康桑格研究所(Wellcome Sanger Institute)的上述研究發現「令人震驚」而且「發人深省」。基因變異指關於我們身體成長和管理的相關指令，也就是DNA當中發生的變化。長期以來，變異一直被當作癌症的根源，但是數十年來關於變異是否對衰老有重要影響一直存在爭論。桑格研究所的研究人員說他們得到了「第一個試驗性證據」，證明了兩者的關係。他們分析了預期壽命各異的物種基因變異的不同速度。研究人員分析了貓、黑白疣猴、狗、雪貂、長頸鹿、馬、人、獅子、裸鼴鼠、兔子、老鼠、環尾狐猴和老虎等十幾種動物的DNA。發表在《自然》雜誌上的研究顯示，老鼠在短暫的生命當中每年經歷了將近800次變異，老鼠的壽命一般不到４年。"
---
---
license: apache-2.0
tags:
- Summarization
metrics:
- rouge
model-index:
- name: best_model_test_0423_small
results: []
---
# best_model_test_0423_small
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6341
- Rouge1: 18.7681
- Rouge2: 6.3762
- Rougel: 18.6081
- Rougelsum: 18.6173
- Gen Len: 22.1086
## 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: 0.0001
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 5.8165 | 0.05 | 1000 | 3.6541 | 11.6734 | 3.9865 | 11.5734 | 11.5375 | 18.0056 |
| 4.306 | 0.1 | 2000 | 3.4291 | 12.0417 | 3.8419 | 11.9231 | 11.9223 | 16.8948 |
| 4.1091 | 0.16 | 3000 | 3.3643 | 13.661 | 4.5171 | 13.5123 | 13.5076 | 19.4016 |
| 3.9637 | 0.21 | 4000 | 3.2574 | 13.8443 | 4.1761 | 13.689 | 13.6927 | 18.4288 |
| 3.8205 | 0.26 | 5000 | 3.2434 | 13.5371 | 4.3639 | 13.3551 | 13.3552 | 21.5776 |
| 3.7262 | 0.31 | 6000 | 3.1690 | 14.3668 | 4.8048 | 14.2191 | 14.1906 | 21.5548 |
| 3.6887 | 0.36 | 7000 | 3.0657 | 14.3265 | 4.436 | 14.212 | 14.205 | 20.89 |
| 3.6337 | 0.42 | 8000 | 3.0318 | 14.6809 | 4.8345 | 14.5378 | 14.5331 | 20.3651 |
| 3.5443 | 0.47 | 9000 | 3.0554 | 15.3372 | 4.9163 | 15.1794 | 15.1781 | 21.7742 |
| 3.5203 | 0.52 | 10000 | 2.9793 | 14.9278 | 4.9656 | 14.7491 | 14.743 | 20.8113 |
| 3.4936 | 0.57 | 11000 | 3.0079 | 15.7705 | 5.1453 | 15.5582 | 15.5756 | 23.4274 |
| 3.4592 | 0.62 | 12000 | 2.9721 | 15.0201 | 5.1612 | 14.8508 | 14.8198 | 22.7007 |
| 3.377 | 0.67 | 13000 | 3.0112 | 15.9595 | 5.1133 | 15.78 | 15.7774 | 23.4427 |
| 3.4158 | 0.73 | 14000 | 2.9239 | 14.7984 | 5.051 | 14.6943 | 14.6581 | 21.6009 |
| 3.378 | 0.78 | 15000 | 2.8897 | 16.5128 | 5.1923 | 16.3523 | 16.3265 | 22.0828 |
| 3.3231 | 0.83 | 16000 | 2.9347 | 16.9997 | 5.5524 | 16.8534 | 16.8737 | 22.5807 |
| 3.3268 | 0.88 | 17000 | 2.9116 | 16.0261 | 5.4226 | 15.9234 | 15.914 | 23.6988 |
| 3.3127 | 0.93 | 18000 | 2.8610 | 16.6255 | 5.3554 | 16.4729 | 16.4569 | 22.9481 |
| 3.2664 | 0.99 | 19000 | 2.8606 | 17.7703 | 5.9475 | 17.6229 | 17.6259 | 23.4423 |
| 3.1718 | 1.04 | 20000 | 2.8764 | 17.301 | 5.6262 | 17.122 | 17.1104 | 23.0093 |
| 3.0987 | 1.09 | 21000 | 2.8282 | 16.4718 | 5.2077 | 16.3394 | 16.3401 | 20.9697 |
| 3.1486 | 1.14 | 22000 | 2.8235 | 18.5594 | 5.9469 | 18.3882 | 18.3799 | 22.7291 |
| 3.1435 | 1.19 | 23000 | 2.8261 | 18.111 | 6.0309 | 17.9593 | 17.9613 | 22.9612 |
| 3.1049 | 1.25 | 24000 | 2.8068 | 17.124 | 5.5675 | 16.9714 | 16.9876 | 22.5558 |
| 3.1357 | 1.3 | 25000 | 2.8014 | 17.3916 | 5.8671 | 17.2148 | 17.2502 | 23.0075 |
| 3.0904 | 1.35 | 26000 | 2.7790 | 17.419 | 5.6689 | 17.3125 | 17.3058 | 22.1492 |
| 3.0877 | 1.4 | 27000 | 2.7462 | 17.0605 | 5.4735 | 16.9414 | 16.9378 | 21.7522 |
| 3.0694 | 1.45 | 28000 | 2.7563 | 17.752 | 5.8889 | 17.5967 | 17.619 | 23.2005 |
| 3.0498 | 1.51 | 29000 | 2.7521 | 17.9056 | 5.7754 | 17.7624 | 17.7836 | 21.9369 |
| 3.0566 | 1.56 | 30000 | 2.7468 | 18.6531 | 6.0538 | 18.5397 | 18.5038 | 22.2358 |
| 3.0489 | 1.61 | 31000 | 2.7450 | 18.4869 | 5.9297 | 18.3139 | 18.3169 | 22.0108 |
| 3.0247 | 1.66 | 32000 | 2.7449 | 18.5192 | 5.9966 | 18.3721 | 18.3569 | 22.2071 |
| 2.9877 | 1.71 | 33000 | 2.7160 | 18.1655 | 5.9294 | 18.0304 | 18.0836 | 21.4595 |
| 3.0383 | 1.76 | 34000 | 2.7202 | 18.4959 | 6.2413 | 18.3363 | 18.3431 | 22.9732 |
| 3.041 | 1.82 | 35000 | 2.6948 | 17.5306 | 5.8119 | 17.4011 | 17.4149 | 21.9435 |
| 2.9285 | 1.87 | 36000 | 2.6957 | 18.6418 | 6.1394 | 18.514 | 18.4823 | 22.5174 |
| 3.0556 | 1.92 | 37000 | 2.7000 | 18.7387 | 6.0585 | 18.5761 | 18.574 | 22.9315 |
| 3.0033 | 1.97 | 38000 | 2.6974 | 17.9387 | 6.1387 | 17.8271 | 17.8111 | 22.4726 |
| 2.9207 | 2.02 | 39000 | 2.6998 | 18.6073 | 6.1906 | 18.3891 | 18.4103 | 23.0274 |
| 2.8922 | 2.08 | 40000 | 2.6798 | 18.4017 | 6.2244 | 18.2321 | 18.2296 | 22.0697 |
| 2.8938 | 2.13 | 41000 | 2.6666 | 18.8016 | 6.2066 | 18.6411 | 18.6353 | 21.7017 |
| 2.9124 | 2.18 | 42000 | 2.6606 | 18.7544 | 6.3533 | 18.5923 | 18.5739 | 21.4303 |
| 2.8597 | 2.23 | 43000 | 2.6947 | 18.8672 | 6.4526 | 18.7416 | 18.7482 | 22.3352 |
| 2.8435 | 2.28 | 44000 | 2.6738 | 18.9405 | 6.356 | 18.7791 | 18.7729 | 21.9081 |
| 2.8672 | 2.34 | 45000 | 2.6734 | 18.7509 | 6.3991 | 18.6175 | 18.5828 | 21.8869 |
| 2.899 | 2.39 | 46000 | 2.6575 | 18.5529 | 6.3489 | 18.4139 | 18.401 | 21.7694 |
| 2.8616 | 2.44 | 47000 | 2.6485 | 18.7563 | 6.268 | 18.6368 | 18.6253 | 21.5685 |
| 2.8937 | 2.49 | 48000 | 2.6486 | 18.6525 | 6.3426 | 18.5184 | 18.5129 | 22.3337 |
| 2.8446 | 2.54 | 49000 | 2.6572 | 18.6529 | 6.2655 | 18.4915 | 18.4764 | 22.3331 |
| 2.8676 | 2.59 | 50000 | 2.6608 | 19.0913 | 6.494 | 18.929 | 18.9233 | 22.132 |
| 2.8794 | 2.65 | 51000 | 2.6583 | 18.7648 | 6.459 | 18.6276 | 18.6125 | 22.2414 |
| 2.8836 | 2.7 | 52000 | 2.6512 | 18.7243 | 6.3865 | 18.5848 | 18.5763 | 22.2551 |
| 2.8174 | 2.75 | 53000 | 2.6409 | 18.9393 | 6.3914 | 18.7733 | 18.7715 | 22.1243 |
| 2.8494 | 2.8 | 54000 | 2.6396 | 18.6126 | 6.4389 | 18.4673 | 18.4516 | 21.7638 |
| 2.9025 | 2.85 | 55000 | 2.6341 | 18.7681 | 6.3762 | 18.6081 | 18.6173 | 22.1086 |
| 2.8754 | 2.91 | 56000 | 2.6388 | 19.0828 | 6.5203 | 18.9334 | 18.9285 | 22.3497 |
| 2.8489 | 2.96 | 57000 | 2.6375 | 18.9219 | 6.4922 | 18.763 | 18.7437 | 21.9321 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.1+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
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