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# t5-small-thaisum

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9925
- Rouge1: 0.1865
- Rouge2: 0.0712
- Rougel: 0.184
- Rougelsum: 0.1834
- Gen Len: 17.465

## 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: 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: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 200   | 0.5551          | 0.0154 | 0.0112 | 0.0146 | 0.0148    | 18.8775 |
| No log        | 2.0   | 400   | 0.5326          | 0.0461 | 0.0322 | 0.0453 | 0.0448    | 18.975  |
| 0.5806        | 3.0   | 600   | 0.5353          | 0.0297 | 0.0104 | 0.0285 | 0.0283    | 18.8225 |
| 0.5806        | 4.0   | 800   | 0.5345          | 0.0596 | 0.0396 | 0.0585 | 0.0589    | 18.5025 |
| 0.5104        | 5.0   | 1000  | 0.5230          | 0.0464 | 0.031  | 0.0458 | 0.0454    | 18.9075 |
| 0.5104        | 6.0   | 1200  | 0.5138          | 0.0269 | 0.0128 | 0.0255 | 0.0258    | 18.9075 |
| 0.5104        | 7.0   | 1400  | 0.5186          | 0.0389 | 0.0215 | 0.0379 | 0.0382    | 18.5075 |
| 0.472         | 8.0   | 1600  | 0.5213          | 0.0478 | 0.0266 | 0.0451 | 0.0454    | 18.6375 |
| 0.472         | 9.0   | 1800  | 0.5284          | 0.0608 | 0.0286 | 0.0586 | 0.0584    | 18.8225 |
| 0.4389        | 10.0  | 2000  | 0.5352          | 0.0494 | 0.0272 | 0.0477 | 0.0476    | 18.8925 |
| 0.4389        | 11.0  | 2200  | 0.5372          | 0.0957 | 0.0477 | 0.0947 | 0.0939    | 18.59   |
| 0.4389        | 12.0  | 2400  | 0.5380          | 0.0587 | 0.0303 | 0.0582 | 0.0582    | 18.3225 |
| 0.413         | 13.0  | 2600  | 0.5547          | 0.1026 | 0.0694 | 0.1018 | 0.1017    | 18.2725 |
| 0.413         | 14.0  | 2800  | 0.5374          | 0.0749 | 0.0368 | 0.0737 | 0.0736    | 18.555  |
| 0.3963        | 15.0  | 3000  | 0.5469          | 0.0736 | 0.0311 | 0.0729 | 0.073     | 18.5675 |
| 0.3963        | 16.0  | 3200  | 0.5541          | 0.0685 | 0.0351 | 0.0689 | 0.0687    | 18.6925 |
| 0.3963        | 17.0  | 3400  | 0.5654          | 0.116  | 0.0654 | 0.1148 | 0.1137    | 18.105  |
| 0.3711        | 18.0  | 3600  | 0.5666          | 0.0849 | 0.0389 | 0.0852 | 0.0847    | 18.6025 |
| 0.3711        | 19.0  | 3800  | 0.5739          | 0.1234 | 0.0618 | 0.1205 | 0.1195    | 18.5825 |
| 0.3572        | 20.0  | 4000  | 0.5605          | 0.1121 | 0.0522 | 0.1124 | 0.1112    | 18.405  |
| 0.3572        | 21.0  | 4200  | 0.5952          | 0.117  | 0.0508 | 0.1152 | 0.1146    | 18.4525 |
| 0.3572        | 22.0  | 4400  | 0.5673          | 0.0876 | 0.0385 | 0.0867 | 0.0866    | 18.6025 |
| 0.3367        | 23.0  | 4600  | 0.5772          | 0.0922 | 0.0319 | 0.093  | 0.0928    | 18.57   |
| 0.3367        | 24.0  | 4800  | 0.5908          | 0.1137 | 0.0495 | 0.1134 | 0.1136    | 18.0325 |
| 0.3238        | 25.0  | 5000  | 0.5958          | 0.1197 | 0.051  | 0.118  | 0.1176    | 18.285  |
| 0.3238        | 26.0  | 5200  | 0.5943          | 0.109  | 0.046  | 0.1064 | 0.1058    | 18.0575 |
| 0.3238        | 27.0  | 5400  | 0.6297          | 0.154  | 0.0621 | 0.1503 | 0.1489    | 18.06   |
| 0.3089        | 28.0  | 5600  | 0.6307          | 0.1306 | 0.0494 | 0.1302 | 0.13      | 18.2325 |
| 0.3089        | 29.0  | 5800  | 0.6342          | 0.1441 | 0.0606 | 0.1413 | 0.1415    | 17.5975 |
| 0.2965        | 30.0  | 6000  | 0.6426          | 0.156  | 0.0648 | 0.1492 | 0.1488    | 18.2425 |
| 0.2965        | 31.0  | 6200  | 0.6459          | 0.1533 | 0.0618 | 0.15   | 0.1501    | 17.9225 |
| 0.2965        | 32.0  | 6400  | 0.6498          | 0.1506 | 0.0582 | 0.1482 | 0.1476    | 18.2    |
| 0.283         | 33.0  | 6600  | 0.6566          | 0.1609 | 0.0659 | 0.1561 | 0.1555    | 18.0    |
| 0.283         | 34.0  | 6800  | 0.6616          | 0.1594 | 0.0611 | 0.156  | 0.1559    | 18.08   |
| 0.2757        | 35.0  | 7000  | 0.6814          | 0.1657 | 0.0712 | 0.1613 | 0.1615    | 18.01   |
| 0.2757        | 36.0  | 7200  | 0.6919          | 0.1415 | 0.06   | 0.1372 | 0.1374    | 18.285  |
| 0.2757        | 37.0  | 7400  | 0.6819          | 0.1262 | 0.0501 | 0.126  | 0.1265    | 18.2125 |
| 0.2598        | 38.0  | 7600  | 0.6896          | 0.1354 | 0.0616 | 0.1336 | 0.1341    | 17.8525 |
| 0.2598        | 39.0  | 7800  | 0.7094          | 0.1497 | 0.0706 | 0.1478 | 0.1483    | 18.0175 |
| 0.2508        | 40.0  | 8000  | 0.6995          | 0.1563 | 0.0668 | 0.1547 | 0.155     | 17.9175 |
| 0.2508        | 41.0  | 8200  | 0.7236          | 0.1544 | 0.065  | 0.1536 | 0.154     | 17.37   |
| 0.2508        | 42.0  | 8400  | 0.7196          | 0.1759 | 0.0708 | 0.1723 | 0.1723    | 18.1225 |
| 0.2408        | 43.0  | 8600  | 0.7376          | 0.1692 | 0.0675 | 0.1684 | 0.1694    | 17.6075 |
| 0.2408        | 44.0  | 8800  | 0.7399          | 0.1715 | 0.0709 | 0.1682 | 0.1697    | 17.475  |
| 0.235         | 45.0  | 9000  | 0.7446          | 0.1759 | 0.0733 | 0.1724 | 0.1725    | 17.875  |
| 0.235         | 46.0  | 9200  | 0.7317          | 0.1663 | 0.0763 | 0.1656 | 0.1662    | 17.7375 |
| 0.235         | 47.0  | 9400  | 0.7472          | 0.1719 | 0.0684 | 0.1682 | 0.1685    | 17.94   |
| 0.2224        | 48.0  | 9600  | 0.7423          | 0.1743 | 0.0774 | 0.1725 | 0.1722    | 17.88   |
| 0.2224        | 49.0  | 9800  | 0.7450          | 0.1623 | 0.0723 | 0.1613 | 0.1619    | 17.9675 |
| 0.219         | 50.0  | 10000 | 0.7754          | 0.1442 | 0.0613 | 0.1437 | 0.1436    | 17.86   |
| 0.219         | 51.0  | 10200 | 0.7788          | 0.1652 | 0.0762 | 0.1622 | 0.1622    | 17.415  |
| 0.219         | 52.0  | 10400 | 0.7741          | 0.1655 | 0.0677 | 0.162  | 0.1633    | 17.9625 |
| 0.209         | 53.0  | 10600 | 0.7954          | 0.1803 | 0.076  | 0.1782 | 0.1784    | 18.0875 |
| 0.209         | 54.0  | 10800 | 0.8134          | 0.1639 | 0.0758 | 0.1623 | 0.1621    | 17.6575 |
| 0.2037        | 55.0  | 11000 | 0.8207          | 0.1621 | 0.0665 | 0.1614 | 0.1617    | 17.595  |
| 0.2037        | 56.0  | 11200 | 0.8326          | 0.1549 | 0.0701 | 0.1549 | 0.1549    | 17.7675 |
| 0.2037        | 57.0  | 11400 | 0.8264          | 0.1801 | 0.0807 | 0.1781 | 0.1778    | 17.5775 |
| 0.1962        | 58.0  | 11600 | 0.8321          | 0.167  | 0.0734 | 0.1663 | 0.1658    | 17.7975 |
| 0.1962        | 59.0  | 11800 | 0.8283          | 0.171  | 0.0713 | 0.1681 | 0.1675    | 17.6725 |
| 0.1888        | 60.0  | 12000 | 0.8349          | 0.1657 | 0.0761 | 0.1641 | 0.1647    | 17.5625 |
| 0.1888        | 61.0  | 12200 | 0.8458          | 0.1818 | 0.0799 | 0.1811 | 0.18      | 17.79   |
| 0.1888        | 62.0  | 12400 | 0.8693          | 0.1979 | 0.0784 | 0.1943 | 0.1938    | 17.52   |
| 0.1832        | 63.0  | 12600 | 0.8598          | 0.1766 | 0.0812 | 0.1762 | 0.1754    | 17.87   |
| 0.1832        | 64.0  | 12800 | 0.8845          | 0.1694 | 0.073  | 0.1688 | 0.1682    | 17.5725 |
| 0.1785        | 65.0  | 13000 | 0.8745          | 0.1909 | 0.0747 | 0.1881 | 0.1881    | 17.51   |
| 0.1785        | 66.0  | 13200 | 0.8929          | 0.1812 | 0.0715 | 0.1808 | 0.1807    | 17.34   |
| 0.1785        | 67.0  | 13400 | 0.8821          | 0.1831 | 0.0713 | 0.1813 | 0.1813    | 17.4575 |
| 0.174         | 68.0  | 13600 | 0.8809          | 0.197  | 0.0771 | 0.1941 | 0.194     | 17.665  |
| 0.174         | 69.0  | 13800 | 0.8868          | 0.19   | 0.0749 | 0.1859 | 0.1858    | 17.6825 |
| 0.1697        | 70.0  | 14000 | 0.8952          | 0.1853 | 0.0759 | 0.1816 | 0.1815    | 17.44   |
| 0.1697        | 71.0  | 14200 | 0.9106          | 0.1878 | 0.0744 | 0.1834 | 0.1835    | 17.4675 |
| 0.1697        | 72.0  | 14400 | 0.9185          | 0.1883 | 0.0781 | 0.1856 | 0.1863    | 17.5475 |
| 0.1646        | 73.0  | 14600 | 0.9060          | 0.1809 | 0.0731 | 0.1776 | 0.178     | 17.5725 |
| 0.1646        | 74.0  | 14800 | 0.9225          | 0.1822 | 0.0707 | 0.1794 | 0.1798    | 17.255  |
| 0.163         | 75.0  | 15000 | 0.9178          | 0.1759 | 0.0659 | 0.1751 | 0.1755    | 17.5125 |
| 0.163         | 76.0  | 15200 | 0.9366          | 0.1923 | 0.0738 | 0.1883 | 0.1886    | 17.31   |
| 0.163         | 77.0  | 15400 | 0.9387          | 0.1884 | 0.0699 | 0.1852 | 0.1855    | 17.4875 |
| 0.1582        | 78.0  | 15600 | 0.9315          | 0.1869 | 0.0773 | 0.1831 | 0.1834    | 17.5775 |
| 0.1582        | 79.0  | 15800 | 0.9339          | 0.19   | 0.0738 | 0.1862 | 0.1877    | 17.6125 |
| 0.1537        | 80.0  | 16000 | 0.9501          | 0.192  | 0.0744 | 0.1894 | 0.1889    | 17.1625 |
| 0.1537        | 81.0  | 16200 | 0.9403          | 0.1874 | 0.074  | 0.1844 | 0.1845    | 17.5075 |
| 0.1537        | 82.0  | 16400 | 0.9448          | 0.1919 | 0.0771 | 0.1889 | 0.1893    | 17.615  |
| 0.1528        | 83.0  | 16600 | 0.9521          | 0.1924 | 0.0795 | 0.1902 | 0.1908    | 17.685  |
| 0.1528        | 84.0  | 16800 | 0.9518          | 0.1974 | 0.0844 | 0.1944 | 0.1956    | 17.61   |
| 0.1484        | 85.0  | 17000 | 0.9580          | 0.2139 | 0.0815 | 0.2105 | 0.2108    | 17.645  |
| 0.1484        | 86.0  | 17200 | 0.9626          | 0.1871 | 0.0719 | 0.1849 | 0.1846    | 17.5075 |
| 0.1484        | 87.0  | 17400 | 0.9746          | 0.1985 | 0.0795 | 0.1958 | 0.1957    | 17.47   |
| 0.144         | 88.0  | 17600 | 0.9726          | 0.1947 | 0.0771 | 0.1917 | 0.1913    | 17.4475 |
| 0.144         | 89.0  | 17800 | 0.9757          | 0.1986 | 0.0804 | 0.1954 | 0.1946    | 17.4775 |
| 0.1448        | 90.0  | 18000 | 0.9788          | 0.1989 | 0.0826 | 0.1961 | 0.1945    | 17.4775 |
| 0.1448        | 91.0  | 18200 | 0.9776          | 0.1881 | 0.0742 | 0.1855 | 0.1847    | 17.4275 |
| 0.1448        | 92.0  | 18400 | 0.9791          | 0.186  | 0.0698 | 0.183  | 0.1827    | 17.5    |
| 0.1417        | 93.0  | 18600 | 0.9812          | 0.1917 | 0.0746 | 0.1891 | 0.1884    | 17.53   |
| 0.1417        | 94.0  | 18800 | 0.9913          | 0.1937 | 0.0786 | 0.1912 | 0.1905    | 17.4125 |
| 0.1399        | 95.0  | 19000 | 0.9935          | 0.1942 | 0.0814 | 0.1917 | 0.1912    | 17.5425 |
| 0.1399        | 96.0  | 19200 | 0.9887          | 0.1909 | 0.0686 | 0.1882 | 0.1879    | 17.49   |
| 0.1399        | 97.0  | 19400 | 0.9899          | 0.1921 | 0.0745 | 0.1897 | 0.1888    | 17.4275 |
| 0.1399        | 98.0  | 19600 | 0.9910          | 0.1909 | 0.0747 | 0.1879 | 0.187     | 17.4225 |
| 0.1399        | 99.0  | 19800 | 0.9922          | 0.1889 | 0.0738 | 0.1864 | 0.186     | 17.445  |
| 0.1392        | 100.0 | 20000 | 0.9925          | 0.1865 | 0.0712 | 0.184  | 0.1834    | 17.465  |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-thaisum", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-thaisum
================


This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.9925
* Rouge1: 0.1865
* Rouge2: 0.0712
* Rougel: 0.184
* Rougelsum: 0.1834
* Gen Len: 17.465


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: 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: 100


### Training results



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 100 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 100 ### Training results ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3