transformers pytorch mt5 text2text-generation summarization mT5 zh autotrain_compatible endpoints_compatible region:us
---
language:
- zh
tags:
- summarization
- mT5
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
