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# t5-small-thaisum-title-mt5tokenizer

This model is a fine-tuned version of [Nopphakorn/t5-small-thaisum-title-mt5tokenizer](https://huggingface.co/Nopphakorn/t5-small-thaisum-title-mt5tokenizer) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4541
- Rouge1: 0.0552
- Rouge2: 0.0069
- Rougel: 0.0547
- Rougelsum: 0.0546
- Gen Len: 18.9956

## 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: 5e-05
- 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: cosine
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 4.7436        | 1.0   | 765   | 4.8643          | 0.0029 | 0.0    | 0.0029 | 0.0029    | 18.9897 |
| 4.7069        | 2.0   | 1530  | 4.8482          | 0.0114 | 0.0015 | 0.0111 | 0.0115    | 18.9941 |
| 4.6588        | 3.0   | 2295  | 4.8272          | 0.0054 | 0.0    | 0.0055 | 0.0055    | 18.9883 |
| 4.6664        | 4.0   | 3060  | 4.8044          | 0.0116 | 0.0    | 0.0113 | 0.0116    | 18.9853 |
| 4.6283        | 5.0   | 3825  | 4.7903          | 0.0094 | 0.0    | 0.0095 | 0.0093    | 18.9868 |
| 4.6145        | 6.0   | 4590  | 4.7669          | 0.009  | 0.0    | 0.0088 | 0.0088    | 18.9941 |
| 4.5759        | 7.0   | 5355  | 4.7432          | 0.008  | 0.0    | 0.0079 | 0.0079    | 18.9883 |
| 4.5419        | 8.0   | 6120  | 4.7275          | 0.012  | 0.0015 | 0.012  | 0.012     | 18.9941 |
| 4.5486        | 9.0   | 6885  | 4.7043          | 0.0098 | 0.0015 | 0.0099 | 0.0097    | 18.9941 |
| 4.5095        | 10.0  | 7650  | 4.6773          | 0.0085 | 0.0    | 0.0088 | 0.0086    | 18.9941 |
| 4.4682        | 11.0  | 8415  | 4.6561          | 0.0115 | 0.0    | 0.0112 | 0.0114    | 18.9927 |
| 4.4783        | 12.0  | 9180  | 4.6326          | 0.0063 | 0.0    | 0.0063 | 0.0061    | 18.9868 |
| 4.4825        | 13.0  | 9945  | 4.6069          | 0.0111 | 0.0    | 0.0109 | 0.0111    | 18.9838 |
| 4.4455        | 14.0  | 10710 | 4.5836          | 0.0086 | 0.0    | 0.0084 | 0.0085    | 18.9941 |
| 4.4328        | 15.0  | 11475 | 4.5587          | 0.0089 | 0.0    | 0.0091 | 0.0088    | 18.9912 |
| 4.3982        | 16.0  | 12240 | 4.5294          | 0.0111 | 0.0    | 0.0112 | 0.0112    | 18.9868 |
| 4.3463        | 17.0  | 13005 | 4.5069          | 0.0135 | 0.0    | 0.0137 | 0.0137    | 18.9853 |
| 4.3379        | 18.0  | 13770 | 4.4717          | 0.011  | 0.0007 | 0.0109 | 0.011     | 18.9897 |
| 4.3303        | 19.0  | 14535 | 4.4460          | 0.0117 | 0.0007 | 0.0119 | 0.0118    | 18.9853 |
| 4.2983        | 20.0  | 15300 | 4.4110          | 0.0107 | 0.0    | 0.0109 | 0.0108    | 18.9868 |
| 4.265         | 21.0  | 16065 | 4.3800          | 0.0095 | 0.0    | 0.0096 | 0.0095    | 18.9941 |
| 4.2507        | 22.0  | 16830 | 4.3528          | 0.0105 | 0.0    | 0.0106 | 0.0104    | 18.9941 |
| 4.2184        | 23.0  | 17595 | 4.3246          | 0.0125 | 0.0007 | 0.0128 | 0.0125    | 18.9941 |
| 4.1746        | 24.0  | 18360 | 4.3013          | 0.0116 | 0.0    | 0.0118 | 0.0115    | 19.0    |
| 4.1744        | 25.0  | 19125 | 4.2724          | 0.0127 | 0.0    | 0.0128 | 0.0127    | 18.9956 |
| 4.1123        | 26.0  | 19890 | 4.2408          | 0.0127 | 0.0    | 0.0128 | 0.0128    | 19.0    |
| 4.131         | 27.0  | 20655 | 4.2045          | 0.0118 | 0.0    | 0.0121 | 0.0118    | 18.9897 |
| 4.0725        | 28.0  | 21420 | 4.1815          | 0.0112 | 0.0    | 0.0113 | 0.011     | 19.0    |
| 4.0439        | 29.0  | 22185 | 4.1452          | 0.0154 | 0.0024 | 0.0154 | 0.0152    | 19.0    |
| 4.0814        | 30.0  | 22950 | 4.1151          | 0.0142 | 0.0007 | 0.0143 | 0.014     | 19.0    |
| 4.009         | 31.0  | 23715 | 4.0963          | 0.015  | 0.0007 | 0.015  | 0.015     | 18.9985 |
| 4.0326        | 32.0  | 24480 | 4.0679          | 0.016  | 0.0    | 0.016  | 0.0161    | 19.0    |
| 3.948         | 33.0  | 25245 | 4.0460          | 0.0137 | 0.0015 | 0.0133 | 0.0132    | 18.9971 |
| 3.9401        | 34.0  | 26010 | 4.0070          | 0.0162 | 0.0024 | 0.0161 | 0.016     | 18.9985 |
| 3.9111        | 35.0  | 26775 | 3.9874          | 0.0209 | 0.0042 | 0.0209 | 0.021     | 18.9985 |
| 3.899         | 36.0  | 27540 | 3.9650          | 0.0185 | 0.0    | 0.0186 | 0.0186    | 18.9985 |
| 3.8821        | 37.0  | 28305 | 3.9457          | 0.0236 | 0.0029 | 0.024  | 0.024     | 19.0    |
| 3.8687        | 38.0  | 29070 | 3.9105          | 0.0241 | 0.0034 | 0.0239 | 0.0238    | 19.0    |
| 3.8076        | 39.0  | 29835 | 3.9029          | 0.0199 | 0.002  | 0.0199 | 0.0201    | 19.0    |
| 3.8063        | 40.0  | 30600 | 3.8750          | 0.0251 | 0.0034 | 0.0244 | 0.0246    | 19.0    |
| 3.7896        | 41.0  | 31365 | 3.8448          | 0.025  | 0.0028 | 0.0248 | 0.0249    | 18.9941 |
| 3.7672        | 42.0  | 32130 | 3.8287          | 0.0336 | 0.0029 | 0.0333 | 0.0332    | 19.0    |
| 3.7919        | 43.0  | 32895 | 3.8022          | 0.0268 | 0.0039 | 0.0268 | 0.0268    | 18.9956 |
| 3.75          | 44.0  | 33660 | 3.7723          | 0.0286 | 0.0044 | 0.0286 | 0.0284    | 18.9971 |
| 3.7263        | 45.0  | 34425 | 3.7630          | 0.0308 | 0.0039 | 0.0308 | 0.0307    | 19.0    |
| 3.7053        | 46.0  | 35190 | 3.7412          | 0.0341 | 0.0037 | 0.0335 | 0.0335    | 19.0    |
| 3.7022        | 47.0  | 35955 | 3.7214          | 0.0347 | 0.0044 | 0.0335 | 0.0336    | 18.9897 |
| 3.6528        | 48.0  | 36720 | 3.7059          | 0.0318 | 0.0044 | 0.032  | 0.032     | 19.0    |
| 3.6614        | 49.0  | 37485 | 3.6833          | 0.0313 | 0.0044 | 0.031  | 0.0309    | 18.9956 |
| 3.6339        | 50.0  | 38250 | 3.6691          | 0.0357 | 0.0051 | 0.0353 | 0.0352    | 18.9853 |
| 3.6153        | 51.0  | 39015 | 3.6500          | 0.0373 | 0.0044 | 0.0363 | 0.0365    | 18.9912 |
| 3.6083        | 52.0  | 39780 | 3.6360          | 0.0358 | 0.0051 | 0.0354 | 0.0354    | 18.9985 |
| 3.5857        | 53.0  | 40545 | 3.6272          | 0.0409 | 0.0044 | 0.0397 | 0.0396    | 19.0    |
| 3.5903        | 54.0  | 41310 | 3.6141          | 0.0455 | 0.0039 | 0.044  | 0.0439    | 18.9956 |
| 3.5429        | 55.0  | 42075 | 3.6044          | 0.0405 | 0.0054 | 0.0393 | 0.0394    | 18.9883 |
| 3.5526        | 56.0  | 42840 | 3.5933          | 0.0379 | 0.0049 | 0.037  | 0.0367    | 18.9883 |
| 3.5075        | 57.0  | 43605 | 3.5820          | 0.0431 | 0.0071 | 0.041  | 0.041     | 18.9985 |
| 3.5233        | 58.0  | 44370 | 3.5698          | 0.045  | 0.0064 | 0.0433 | 0.0434    | 18.9897 |
| 3.5022        | 59.0  | 45135 | 3.5680          | 0.0432 | 0.0061 | 0.0419 | 0.0416    | 18.9941 |
| 3.5258        | 60.0  | 45900 | 3.5604          | 0.047  | 0.0069 | 0.0452 | 0.0453    | 18.9956 |
| 3.4763        | 61.0  | 46665 | 3.5532          | 0.0465 | 0.0069 | 0.0452 | 0.0451    | 18.9985 |
| 3.4591        | 62.0  | 47430 | 3.5468          | 0.0429 | 0.0078 | 0.0425 | 0.0422    | 18.9971 |
| 3.471         | 63.0  | 48195 | 3.5359          | 0.0426 | 0.0078 | 0.0428 | 0.0426    | 18.9971 |
| 3.4671        | 64.0  | 48960 | 3.5348          | 0.0441 | 0.0064 | 0.0437 | 0.0436    | 18.9941 |
| 3.4588        | 65.0  | 49725 | 3.5291          | 0.0436 | 0.0064 | 0.0427 | 0.0426    | 18.9941 |
| 3.4214        | 66.0  | 50490 | 3.5168          | 0.0409 | 0.0071 | 0.0407 | 0.0408    | 18.9956 |
| 3.4531        | 67.0  | 51255 | 3.5091          | 0.0476 | 0.0082 | 0.0477 | 0.0479    | 18.9956 |
| 3.3936        | 68.0  | 52020 | 3.5016          | 0.044  | 0.0073 | 0.0441 | 0.0442    | 18.9956 |
| 3.4113        | 69.0  | 52785 | 3.5028          | 0.0473 | 0.0069 | 0.0472 | 0.0472    | 18.9956 |
| 3.4092        | 70.0  | 53550 | 3.4993          | 0.0483 | 0.0078 | 0.0488 | 0.0485    | 18.9985 |
| 3.3847        | 71.0  | 54315 | 3.4959          | 0.053  | 0.0078 | 0.0524 | 0.0524    | 18.9956 |
| 3.4099        | 72.0  | 55080 | 3.4906          | 0.0549 | 0.0069 | 0.0541 | 0.0542    | 18.9985 |
| 3.3774        | 73.0  | 55845 | 3.4821          | 0.0527 | 0.0064 | 0.052  | 0.052     | 18.9971 |
| 3.3677        | 74.0  | 56610 | 3.4790          | 0.0542 | 0.0069 | 0.0534 | 0.0534    | 18.9956 |
| 3.3707        | 75.0  | 57375 | 3.4747          | 0.0562 | 0.0069 | 0.0556 | 0.0557    | 18.9956 |
| 3.3953        | 76.0  | 58140 | 3.4713          | 0.0567 | 0.0069 | 0.056  | 0.056     | 18.9956 |
| 3.3767        | 77.0  | 58905 | 3.4695          | 0.0559 | 0.0069 | 0.0552 | 0.0551    | 18.9956 |
| 3.3455        | 78.0  | 59670 | 3.4668          | 0.0518 | 0.0069 | 0.0514 | 0.0508    | 18.9985 |
| 3.3749        | 79.0  | 60435 | 3.4648          | 0.055  | 0.0069 | 0.0546 | 0.0545    | 18.9941 |
| 3.3447        | 80.0  | 61200 | 3.4648          | 0.0534 | 0.0069 | 0.0529 | 0.0526    | 18.9956 |
| 3.3892        | 81.0  | 61965 | 3.4643          | 0.0572 | 0.0078 | 0.0568 | 0.0564    | 18.9985 |
| 3.3681        | 82.0  | 62730 | 3.4640          | 0.0545 | 0.0069 | 0.0542 | 0.0537    | 18.9956 |
| 3.3186        | 83.0  | 63495 | 3.4595          | 0.0574 | 0.0069 | 0.0569 | 0.0569    | 18.9956 |
| 3.3422        | 84.0  | 64260 | 3.4598          | 0.0553 | 0.0069 | 0.0549 | 0.0548    | 18.9956 |
| 3.3511        | 85.0  | 65025 | 3.4565          | 0.0563 | 0.0078 | 0.0561 | 0.0561    | 18.9956 |
| 3.3469        | 86.0  | 65790 | 3.4576          | 0.0569 | 0.0069 | 0.0562 | 0.0563    | 18.9956 |
| 3.345         | 87.0  | 66555 | 3.4579          | 0.0553 | 0.0069 | 0.0549 | 0.0548    | 18.9956 |
| 3.3611        | 88.0  | 67320 | 3.4558          | 0.0553 | 0.0069 | 0.0549 | 0.0548    | 18.9956 |
| 3.3423        | 89.0  | 68085 | 3.4559          | 0.0569 | 0.0069 | 0.0562 | 0.0563    | 18.9956 |
| 3.3575        | 90.0  | 68850 | 3.4560          | 0.0553 | 0.0069 | 0.0549 | 0.0548    | 18.9956 |
| 3.3322        | 91.0  | 69615 | 3.4560          | 0.0569 | 0.0069 | 0.0562 | 0.0563    | 18.9956 |
| 3.3303        | 92.0  | 70380 | 3.4551          | 0.0569 | 0.0069 | 0.0562 | 0.0563    | 18.9956 |
| 3.3676        | 93.0  | 71145 | 3.4542          | 0.0569 | 0.0069 | 0.0562 | 0.0563    | 18.9956 |
| 3.3219        | 94.0  | 71910 | 3.4541          | 0.0552 | 0.0069 | 0.0547 | 0.0546    | 18.9956 |
| 3.3563        | 95.0  | 72675 | 3.4540          | 0.0569 | 0.0069 | 0.0562 | 0.0563    | 18.9956 |
| 3.3616        | 96.0  | 73440 | 3.4541          | 0.0569 | 0.0069 | 0.0562 | 0.0563    | 18.9956 |
| 3.3417        | 97.0  | 74205 | 3.4543          | 0.0552 | 0.0069 | 0.0547 | 0.0546    | 18.9956 |
| 3.3683        | 98.0  | 74970 | 3.4541          | 0.0552 | 0.0069 | 0.0547 | 0.0546    | 18.9956 |
| 3.3402        | 99.0  | 75735 | 3.4541          | 0.0552 | 0.0069 | 0.0547 | 0.0546    | 18.9956 |
| 3.3413        | 100.0 | 76500 | 3.4541          | 0.0552 | 0.0069 | 0.0547 | 0.0546    | 18.9956 |


### Framework versions

- Transformers 4.29.2
- 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-title-mt5tokenizer", "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-title-mt5tokenizer
===================================


This model is a fine-tuned version of Nopphakorn/t5-small-thaisum-title-mt5tokenizer on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 3.4541
* Rouge1: 0.0552
* Rouge2: 0.0069
* Rougel: 0.0547
* Rougelsum: 0.0546
* Gen Len: 18.9956


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: 5e-05
* 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: cosine
* num\_epochs: 100


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* 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: cosine
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.29.2
* 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: 5e-05
* 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: cosine
* num\_epochs: 100 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3