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# t5-small-thaisum-512

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: 1.6822
- Rouge1: 0.2353
- Rouge2: 0.098
- Rougel: 0.231
- Rougelsum: 0.2327
- Gen Len: 17.5575

## 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: 0.0005
- 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   | 0.5586          | 0.0115 | 0.0033 | 0.0115 | 0.0115    | 19.0    |
| No log        | 2.0   | 400   | 0.5452          | 0.0231 | 0.0062 | 0.0231 | 0.0234    | 18.9825 |
| 0.5769        | 3.0   | 600   | 0.5324          | 0.0423 | 0.0205 | 0.0414 | 0.0416    | 18.7475 |
| 0.5769        | 4.0   | 800   | 0.5295          | 0.0224 | 0.0058 | 0.0221 | 0.0228    | 18.915  |
| 0.472         | 5.0   | 1000  | 0.5546          | 0.098  | 0.0451 | 0.0966 | 0.0986    | 18.94   |
| 0.472         | 6.0   | 1200  | 0.5497          | 0.0473 | 0.0141 | 0.047  | 0.0484    | 18.68   |
| 0.472         | 7.0   | 1400  | 0.5467          | 0.081  | 0.0362 | 0.0799 | 0.0802    | 18.9725 |
| 0.4121        | 8.0   | 1600  | 0.5658          | 0.1309 | 0.0562 | 0.1282 | 0.1286    | 18.6925 |
| 0.4121        | 9.0   | 1800  | 0.5641          | 0.1282 | 0.0549 | 0.1278 | 0.1284    | 18.48   |
| 0.3662        | 10.0  | 2000  | 0.5760          | 0.1537 | 0.0821 | 0.1525 | 0.1536    | 18.615  |
| 0.3662        | 11.0  | 2200  | 0.6014          | 0.175  | 0.0735 | 0.1723 | 0.1732    | 18.3675 |
| 0.3662        | 12.0  | 2400  | 0.6181          | 0.1643 | 0.0699 | 0.1637 | 0.165     | 18.3275 |
| 0.3231        | 13.0  | 2600  | 0.6355          | 0.1349 | 0.0526 | 0.1331 | 0.1342    | 18.6325 |
| 0.3231        | 14.0  | 2800  | 0.6370          | 0.1656 | 0.083  | 0.1621 | 0.1637    | 17.5725 |
| 0.2906        | 15.0  | 3000  | 0.6326          | 0.173  | 0.0756 | 0.1708 | 0.1729    | 17.925  |
| 0.2906        | 16.0  | 3200  | 0.6877          | 0.1719 | 0.0722 | 0.1675 | 0.1687    | 18.0375 |
| 0.2906        | 17.0  | 3400  | 0.7144          | 0.2073 | 0.0888 | 0.2026 | 0.2045    | 17.5625 |
| 0.2511        | 18.0  | 3600  | 0.7148          | 0.1932 | 0.0731 | 0.1904 | 0.1916    | 17.7    |
| 0.2511        | 19.0  | 3800  | 0.7166          | 0.1969 | 0.0823 | 0.1938 | 0.1941    | 17.835  |
| 0.2335        | 20.0  | 4000  | 0.7319          | 0.201  | 0.0798 | 0.2001 | 0.2015    | 17.9775 |
| 0.2335        | 21.0  | 4200  | 0.7522          | 0.2027 | 0.0827 | 0.1999 | 0.2014    | 18.3925 |
| 0.2335        | 22.0  | 4400  | 0.7960          | 0.1904 | 0.0769 | 0.1867 | 0.188     | 18.045  |
| 0.209         | 23.0  | 4600  | 0.7966          | 0.2263 | 0.1026 | 0.2247 | 0.226     | 18.0275 |
| 0.209         | 24.0  | 4800  | 0.8202          | 0.2255 | 0.0924 | 0.2213 | 0.2223    | 17.535  |
| 0.1899        | 25.0  | 5000  | 0.8518          | 0.2181 | 0.0938 | 0.2159 | 0.2167    | 17.875  |
| 0.1899        | 26.0  | 5200  | 0.8737          | 0.2246 | 0.1013 | 0.2223 | 0.2231    | 17.385  |
| 0.1899        | 27.0  | 5400  | 0.8647          | 0.2237 | 0.1025 | 0.2188 | 0.2206    | 17.525  |
| 0.1722        | 28.0  | 5600  | 0.8828          | 0.2058 | 0.0777 | 0.2014 | 0.2033    | 17.82   |
| 0.1722        | 29.0  | 5800  | 0.9233          | 0.2164 | 0.0895 | 0.2127 | 0.2131    | 16.915  |
| 0.1559        | 30.0  | 6000  | 0.9197          | 0.2141 | 0.0898 | 0.206  | 0.2074    | 16.7375 |
| 0.1559        | 31.0  | 6200  | 0.9224          | 0.215  | 0.0951 | 0.2138 | 0.2148    | 17.9725 |
| 0.1559        | 32.0  | 6400  | 0.9181          | 0.2124 | 0.0837 | 0.2081 | 0.2091    | 17.5275 |
| 0.1443        | 33.0  | 6600  | 0.9495          | 0.2124 | 0.0853 | 0.2095 | 0.2099    | 17.6625 |
| 0.1443        | 34.0  | 6800  | 0.9250          | 0.1986 | 0.0729 | 0.1951 | 0.1963    | 17.7725 |
| 0.1355        | 35.0  | 7000  | 0.9943          | 0.1925 | 0.0787 | 0.189  | 0.1905    | 17.46   |
| 0.1355        | 36.0  | 7200  | 0.9961          | 0.2146 | 0.0904 | 0.2117 | 0.2129    | 16.855  |
| 0.1355        | 37.0  | 7400  | 0.9963          | 0.2035 | 0.0742 | 0.1989 | 0.1998    | 17.4725 |
| 0.1256        | 38.0  | 7600  | 1.0356          | 0.2343 | 0.0908 | 0.2274 | 0.2287    | 17.3825 |
| 0.1256        | 39.0  | 7800  | 1.0512          | 0.2234 | 0.0978 | 0.2187 | 0.2196    | 18.08   |
| 0.1153        | 40.0  | 8000  | 1.0227          | 0.2321 | 0.0978 | 0.2273 | 0.2284    | 16.855  |
| 0.1153        | 41.0  | 8200  | 1.0955          | 0.2265 | 0.0928 | 0.2193 | 0.221     | 17.8775 |
| 0.1153        | 42.0  | 8400  | 1.0699          | 0.2131 | 0.0922 | 0.2084 | 0.2097    | 17.535  |
| 0.1057        | 43.0  | 8600  | 1.1177          | 0.2375 | 0.0962 | 0.2317 | 0.2325    | 17.5875 |
| 0.1057        | 44.0  | 8800  | 1.1074          | 0.2473 | 0.1    | 0.2427 | 0.2421    | 17.5875 |
| 0.1           | 45.0  | 9000  | 1.1022          | 0.2408 | 0.0989 | 0.232  | 0.2345    | 17.22   |
| 0.1           | 46.0  | 9200  | 1.1364          | 0.2427 | 0.1026 | 0.2362 | 0.2371    | 17.62   |
| 0.1           | 47.0  | 9400  | 1.0741          | 0.231  | 0.086  | 0.227  | 0.2282    | 17.82   |
| 0.0947        | 48.0  | 9600  | 1.1516          | 0.2443 | 0.1083 | 0.2385 | 0.2402    | 17.3725 |
| 0.0947        | 49.0  | 9800  | 1.1216          | 0.2192 | 0.0823 | 0.2142 | 0.2155    | 17.56   |
| 0.0905        | 50.0  | 10000 | 1.1242          | 0.2215 | 0.0895 | 0.2151 | 0.2155    | 17.3325 |
| 0.0905        | 51.0  | 10200 | 1.1732          | 0.2142 | 0.0895 | 0.2106 | 0.2119    | 17.055  |
| 0.0905        | 52.0  | 10400 | 1.1463          | 0.2294 | 0.0991 | 0.2255 | 0.227     | 17.85   |
| 0.0829        | 53.0  | 10600 | 1.1870          | 0.2167 | 0.091  | 0.2133 | 0.2146    | 17.58   |
| 0.0829        | 54.0  | 10800 | 1.1741          | 0.2322 | 0.095  | 0.2254 | 0.2275    | 17.2925 |
| 0.0797        | 55.0  | 11000 | 1.1595          | 0.2234 | 0.0904 | 0.2174 | 0.22      | 17.2625 |
| 0.0797        | 56.0  | 11200 | 1.2061          | 0.2296 | 0.0982 | 0.2255 | 0.2287    | 17.5525 |
| 0.0797        | 57.0  | 11400 | 1.2275          | 0.2282 | 0.0924 | 0.2243 | 0.2267    | 17.2825 |
| 0.0734        | 58.0  | 11600 | 1.2205          | 0.2111 | 0.0821 | 0.2063 | 0.2085    | 17.4775 |
| 0.0734        | 59.0  | 11800 | 1.2248          | 0.2195 | 0.0901 | 0.2143 | 0.2164    | 17.3475 |
| 0.0691        | 60.0  | 12000 | 1.2842          | 0.2168 | 0.0821 | 0.2127 | 0.2151    | 17.5925 |
| 0.0691        | 61.0  | 12200 | 1.2827          | 0.2357 | 0.0999 | 0.2319 | 0.2327    | 17.1875 |
| 0.0691        | 62.0  | 12400 | 1.3232          | 0.2369 | 0.1    | 0.2309 | 0.2335    | 17.3975 |
| 0.0669        | 63.0  | 12600 | 1.2934          | 0.2279 | 0.091  | 0.2239 | 0.2255    | 17.7175 |
| 0.0669        | 64.0  | 12800 | 1.3149          | 0.234  | 0.0915 | 0.2277 | 0.2294    | 17.62   |
| 0.0602        | 65.0  | 13000 | 1.3568          | 0.2423 | 0.0954 | 0.2362 | 0.2375    | 17.335  |
| 0.0602        | 66.0  | 13200 | 1.3548          | 0.2439 | 0.0997 | 0.2373 | 0.2397    | 17.4825 |
| 0.0602        | 67.0  | 13400 | 1.3380          | 0.2287 | 0.0868 | 0.2238 | 0.2259    | 17.64   |
| 0.0572        | 68.0  | 13600 | 1.3396          | 0.228  | 0.0861 | 0.221  | 0.2231    | 17.3025 |
| 0.0572        | 69.0  | 13800 | 1.3772          | 0.2375 | 0.099  | 0.2311 | 0.2317    | 17.31   |
| 0.0528        | 70.0  | 14000 | 1.3955          | 0.2344 | 0.0866 | 0.228  | 0.2298    | 17.3325 |
| 0.0528        | 71.0  | 14200 | 1.3739          | 0.2231 | 0.0837 | 0.2184 | 0.2203    | 17.4925 |
| 0.0528        | 72.0  | 14400 | 1.4183          | 0.2357 | 0.0909 | 0.2298 | 0.2313    | 17.41   |
| 0.0515        | 73.0  | 14600 | 1.4263          | 0.2287 | 0.0889 | 0.224  | 0.2256    | 17.3    |
| 0.0515        | 74.0  | 14800 | 1.4472          | 0.2427 | 0.0941 | 0.2374 | 0.2383    | 17.4925 |
| 0.0464        | 75.0  | 15000 | 1.4343          | 0.2279 | 0.091  | 0.2233 | 0.2241    | 17.4475 |
| 0.0464        | 76.0  | 15200 | 1.4434          | 0.235  | 0.0962 | 0.229  | 0.2301    | 17.3775 |
| 0.0464        | 77.0  | 15400 | 1.4450          | 0.2352 | 0.0876 | 0.2294 | 0.2312    | 17.3275 |
| 0.0451        | 78.0  | 15600 | 1.4874          | 0.2425 | 0.093  | 0.2382 | 0.2398    | 17.3125 |
| 0.0451        | 79.0  | 15800 | 1.4704          | 0.2296 | 0.0826 | 0.2257 | 0.2266    | 17.7475 |
| 0.0403        | 80.0  | 16000 | 1.5066          | 0.2344 | 0.0935 | 0.2315 | 0.2321    | 17.5    |
| 0.0403        | 81.0  | 16200 | 1.5247          | 0.2378 | 0.0919 | 0.233  | 0.2342    | 17.4375 |
| 0.0403        | 82.0  | 16400 | 1.5434          | 0.2347 | 0.0897 | 0.2303 | 0.2316    | 17.69   |
| 0.0382        | 83.0  | 16600 | 1.5366          | 0.2316 | 0.0986 | 0.2267 | 0.2276    | 17.365  |
| 0.0382        | 84.0  | 16800 | 1.5463          | 0.2408 | 0.0939 | 0.2371 | 0.2383    | 17.465  |
| 0.036         | 85.0  | 17000 | 1.5652          | 0.2319 | 0.0939 | 0.2277 | 0.2288    | 17.6075 |
| 0.036         | 86.0  | 17200 | 1.5848          | 0.2293 | 0.0952 | 0.2246 | 0.2255    | 17.43   |
| 0.036         | 87.0  | 17400 | 1.6144          | 0.239  | 0.1035 | 0.2343 | 0.2349    | 17.4425 |
| 0.0332        | 88.0  | 17600 | 1.5723          | 0.2326 | 0.0961 | 0.228  | 0.2299    | 17.495  |
| 0.0332        | 89.0  | 17800 | 1.5910          | 0.2373 | 0.0998 | 0.231  | 0.2326    | 17.3    |
| 0.0312        | 90.0  | 18000 | 1.6275          | 0.2362 | 0.0989 | 0.2314 | 0.2325    | 17.5525 |
| 0.0312        | 91.0  | 18200 | 1.6320          | 0.2367 | 0.0986 | 0.2326 | 0.2336    | 17.4925 |
| 0.0312        | 92.0  | 18400 | 1.6487          | 0.2353 | 0.0986 | 0.2299 | 0.2321    | 17.465  |
| 0.0286        | 93.0  | 18600 | 1.6535          | 0.2359 | 0.0979 | 0.2315 | 0.2336    | 17.5575 |
| 0.0286        | 94.0  | 18800 | 1.6568          | 0.2406 | 0.0977 | 0.2356 | 0.237     | 17.63   |
| 0.0274        | 95.0  | 19000 | 1.6588          | 0.2359 | 0.0956 | 0.2314 | 0.2339    | 17.5575 |
| 0.0274        | 96.0  | 19200 | 1.6772          | 0.2442 | 0.0996 | 0.2397 | 0.2411    | 17.5675 |
| 0.0274        | 97.0  | 19400 | 1.6732          | 0.2344 | 0.0949 | 0.2304 | 0.2313    | 17.4925 |
| 0.025         | 98.0  | 19600 | 1.6801          | 0.2329 | 0.096  | 0.2287 | 0.2299    | 17.5525 |
| 0.025         | 99.0  | 19800 | 1.6837          | 0.2349 | 0.098  | 0.2306 | 0.2323    | 17.545  |
| 0.0245        | 100.0 | 20000 | 1.6822          | 0.2353 | 0.098  | 0.231  | 0.2327    | 17.5575 |


### 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-512", "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-512
====================


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


* Loss: 1.6822
* Rouge1: 0.2353
* Rouge2: 0.098
* Rougel: 0.231
* Rougelsum: 0.2327
* Gen Len: 17.5575


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: 0.0005
* 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: 0.0005
* 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: 0.0005
* 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