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# outputs

This model is a fine-tuned version of [mrm8488/t5-base-finetuned-common_gen](https://huggingface.co/mrm8488/t5-base-finetuned-common_gen) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2833
- Rouge1: 79.0721
- Rouge2: 59.355
- Rougel: 70.9787
- Rougelsum: 70.9177
- Gen Len: 16.3819

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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   | 241   | 2.3934          | 75.6585 | 53.2221 | 65.7693 | 65.7796   | 16.4214 |
| No log        | 2.0   | 482   | 2.4223          | 76.75   | 55.466  | 67.3377 | 67.3425   | 16.4381 |
| 0.5668        | 3.0   | 723   | 2.5147          | 77.5314 | 56.5654 | 68.4518 | 68.4263   | 16.4235 |
| 0.5668        | 4.0   | 964   | 2.5164          | 78.3267 | 57.4609 | 69.1759 | 69.1835   | 16.4381 |
| 0.4546        | 5.0   | 1205  | 2.6058          | 78.6699 | 58.2725 | 69.8265 | 69.8802   | 16.358  |
| 0.4546        | 6.0   | 1446  | 2.6295          | 78.8943 | 58.182  | 69.5933 | 69.619    | 16.4464 |
| 0.4014        | 7.0   | 1687  | 2.6592          | 79.0497 | 58.7135 | 70.2046 | 70.2047   | 16.4152 |
| 0.4014        | 8.0   | 1928  | 2.6610          | 79.1756 | 59.143  | 70.5631 | 70.5854   | 16.4235 |
| 0.3652        | 9.0   | 2169  | 2.6636          | 79.2156 | 59.3209 | 70.5418 | 70.5757   | 16.4308 |
| 0.3652        | 10.0  | 2410  | 2.6647          | 78.9922 | 59.0252 | 70.49   | 70.5262   | 16.3569 |
| 0.341         | 11.0  | 2651  | 2.7105          | 79.0372 | 58.7891 | 70.2666 | 70.3333   | 16.4131 |
| 0.341         | 12.0  | 2892  | 2.7829          | 79.2323 | 59.4941 | 70.6877 | 70.7283   | 16.3684 |
| 0.3199        | 13.0  | 3133  | 2.7344          | 79.0326 | 58.8869 | 70.2648 | 70.2983   | 16.4329 |
| 0.3199        | 14.0  | 3374  | 2.7568          | 79.172  | 59.1695 | 70.5229 | 70.5505   | 16.435  |
| 0.301         | 15.0  | 3615  | 2.7773          | 78.9152 | 58.8776 | 70.368  | 70.3922   | 16.3923 |
| 0.301         | 16.0  | 3856  | 2.8178          | 79.1939 | 59.1455 | 70.6938 | 70.7153   | 16.4693 |
| 0.2883        | 17.0  | 4097  | 2.8105          | 79.1307 | 59.114  | 70.6573 | 70.67     | 16.4287 |
| 0.2883        | 18.0  | 4338  | 2.8360          | 78.8425 | 58.6959 | 70.4532 | 70.3955   | 16.358  |
| 0.2779        | 19.0  | 4579  | 2.8606          | 79.3713 | 59.3462 | 70.6531 | 70.648    | 16.3632 |
| 0.2779        | 20.0  | 4820  | 2.8513          | 79.0083 | 58.817  | 70.5779 | 70.5775   | 16.435  |
| 0.2664        | 21.0  | 5061  | 2.8882          | 79.1277 | 59.1771 | 70.8215 | 70.7963   | 16.4194 |
| 0.2664        | 22.0  | 5302  | 2.8852          | 79.0191 | 59.0226 | 70.6604 | 70.6499   | 16.3715 |
| 0.258         | 23.0  | 5543  | 2.9253          | 79.3219 | 59.1689 | 70.8438 | 70.8462   | 16.41   |
| 0.258         | 24.0  | 5784  | 2.9020          | 79.1941 | 59.044  | 70.6849 | 70.6449   | 16.3996 |
| 0.2494        | 25.0  | 6025  | 2.9579          | 79.5266 | 59.8762 | 71.5284 | 71.5085   | 16.4422 |
| 0.2494        | 26.0  | 6266  | 2.9660          | 79.1653 | 59.3299 | 70.7888 | 70.7755   | 16.4475 |
| 0.2415        | 27.0  | 6507  | 2.9682          | 79.3267 | 59.7158 | 71.1062 | 71.0864   | 16.411  |
| 0.2415        | 28.0  | 6748  | 3.0086          | 79.0646 | 59.1926 | 70.8172 | 70.78     | 16.4422 |
| 0.2415        | 29.0  | 6989  | 3.0190          | 78.9127 | 58.8431 | 70.4618 | 70.4597   | 16.4308 |
| 0.2361        | 30.0  | 7230  | 2.9958          | 79.1999 | 59.5494 | 70.9411 | 70.9      | 16.3684 |
| 0.2361        | 31.0  | 7471  | 2.9824          | 79.0219 | 59.0778 | 70.6465 | 70.6445   | 16.3809 |
| 0.2295        | 32.0  | 7712  | 3.0079          | 79.1633 | 59.4146 | 71.0352 | 70.9144   | 16.4017 |
| 0.2295        | 33.0  | 7953  | 2.9894          | 79.106  | 59.0905 | 70.644  | 70.622    | 16.4069 |
| 0.2247        | 34.0  | 8194  | 3.0256          | 78.8789 | 59.0956 | 70.8588 | 70.796    | 16.3954 |
| 0.2247        | 35.0  | 8435  | 3.0451          | 78.7977 | 58.889  | 70.6925 | 70.6408   | 16.3975 |
| 0.2196        | 36.0  | 8676  | 3.0475          | 78.8942 | 58.8944 | 70.4803 | 70.4858   | 16.3871 |
| 0.2196        | 37.0  | 8917  | 3.0289          | 78.7668 | 58.7055 | 70.3606 | 70.3058   | 16.411  |
| 0.2157        | 38.0  | 9158  | 3.0748          | 78.833  | 58.6404 | 70.271  | 70.2338   | 16.4266 |
| 0.2157        | 39.0  | 9399  | 3.0665          | 79.2216 | 59.3481 | 70.9647 | 70.9076   | 16.4058 |
| 0.2104        | 40.0  | 9640  | 3.0773          | 78.9717 | 58.8784 | 70.6392 | 70.6359   | 16.3704 |
| 0.2104        | 41.0  | 9881  | 3.0823          | 78.901  | 58.9511 | 70.737  | 70.7051   | 16.3788 |
| 0.2061        | 42.0  | 10122 | 3.0637          | 78.8689 | 58.8098 | 70.754  | 70.7783   | 16.36   |
| 0.2061        | 43.0  | 10363 | 3.0965          | 79.4409 | 59.5757 | 71.2304 | 71.2553   | 16.4422 |
| 0.2027        | 44.0  | 10604 | 3.1178          | 78.8964 | 58.9388 | 70.5416 | 70.5367   | 16.4048 |
| 0.2027        | 45.0  | 10845 | 3.1078          | 79.2306 | 59.5613 | 71.1202 | 71.0741   | 16.4089 |
| 0.1998        | 46.0  | 11086 | 3.1432          | 79.2393 | 59.5587 | 71.1138 | 71.0502   | 16.4485 |
| 0.1998        | 47.0  | 11327 | 3.1493          | 79.3553 | 59.5023 | 71.0459 | 71.0508   | 16.4256 |
| 0.1966        | 48.0  | 11568 | 3.1670          | 79.0395 | 59.1524 | 70.9374 | 70.921    | 16.4006 |
| 0.1966        | 49.0  | 11809 | 3.1580          | 79.2359 | 59.2038 | 71.0061 | 70.9725   | 16.4568 |
| 0.1941        | 50.0  | 12050 | 3.1458          | 79.1914 | 59.4751 | 71.2448 | 71.2131   | 16.437  |
| 0.1941        | 51.0  | 12291 | 3.1379          | 79.2891 | 59.6776 | 71.3416 | 71.2787   | 16.4287 |
| 0.1903        | 52.0  | 12532 | 3.1618          | 79.4208 | 59.7335 | 71.4722 | 71.4196   | 16.4173 |
| 0.1903        | 53.0  | 12773 | 3.1712          | 79.2976 | 59.736  | 71.1151 | 71.0491   | 16.4454 |
| 0.188         | 54.0  | 13014 | 3.1795          | 79.2538 | 59.2768 | 71.0137 | 70.9602   | 16.4183 |
| 0.188         | 55.0  | 13255 | 3.1836          | 79.4504 | 59.3201 | 71.0908 | 71.0614   | 16.4277 |
| 0.188         | 56.0  | 13496 | 3.1800          | 79.067  | 59.1309 | 70.7069 | 70.6896   | 16.3996 |
| 0.1858        | 57.0  | 13737 | 3.1660          | 79.2369 | 59.5092 | 71.0077 | 70.9537   | 16.3871 |
| 0.1858        | 58.0  | 13978 | 3.1886          | 79.0404 | 58.8652 | 70.6484 | 70.6162   | 16.3798 |
| 0.1849        | 59.0  | 14219 | 3.1413          | 79.1468 | 59.1964 | 70.8362 | 70.8076   | 16.4121 |
| 0.1849        | 60.0  | 14460 | 3.1778          | 79.0189 | 58.9894 | 70.597  | 70.5447   | 16.3788 |
| 0.1824        | 61.0  | 14701 | 3.1674          | 78.8303 | 58.7354 | 70.4517 | 70.4009   | 16.3871 |
| 0.1824        | 62.0  | 14942 | 3.1756          | 78.8914 | 58.7551 | 70.3439 | 70.3031   | 16.3965 |
| 0.1793        | 63.0  | 15183 | 3.1943          | 78.9093 | 58.8599 | 70.415  | 70.3998   | 16.4152 |
| 0.1793        | 64.0  | 15424 | 3.1970          | 78.8353 | 58.8096 | 70.4231 | 70.3813   | 16.4194 |
| 0.1768        | 65.0  | 15665 | 3.2301          | 79.0958 | 59.2143 | 70.837  | 70.7973   | 16.4162 |
| 0.1768        | 66.0  | 15906 | 3.2415          | 79.1112 | 59.1872 | 70.7534 | 70.7145   | 16.4173 |
| 0.177         | 67.0  | 16147 | 3.2332          | 79.0807 | 59.3005 | 70.6878 | 70.6351   | 16.3757 |
| 0.177         | 68.0  | 16388 | 3.2415          | 79.0587 | 59.1872 | 70.6691 | 70.6295   | 16.3819 |
| 0.1744        | 69.0  | 16629 | 3.2454          | 79.3829 | 59.5478 | 70.997  | 70.9835   | 16.4037 |
| 0.1744        | 70.0  | 16870 | 3.2239          | 79.1289 | 59.1661 | 70.7455 | 70.684    | 16.4412 |
| 0.1715        | 71.0  | 17111 | 3.2056          | 79.2074 | 59.1811 | 70.7366 | 70.682    | 16.4339 |
| 0.1715        | 72.0  | 17352 | 3.2101          | 79.1756 | 59.1883 | 70.8208 | 70.821    | 16.4079 |
| 0.1722        | 73.0  | 17593 | 3.2086          | 79.4652 | 59.7631 | 71.3223 | 71.2917   | 16.4214 |
| 0.1722        | 74.0  | 17834 | 3.2269          | 79.0154 | 59.0411 | 70.6754 | 70.6184   | 16.3621 |
| 0.17          | 75.0  | 18075 | 3.2389          | 79.1212 | 59.3143 | 70.7629 | 70.6935   | 16.3444 |
| 0.17          | 76.0  | 18316 | 3.2387          | 79.1148 | 59.4001 | 70.897  | 70.8525   | 16.3663 |
| 0.1682        | 77.0  | 18557 | 3.2505          | 79.0955 | 59.2033 | 70.8455 | 70.8303   | 16.3632 |
| 0.1682        | 78.0  | 18798 | 3.2670          | 79.1781 | 59.1849 | 70.8704 | 70.8286   | 16.3611 |
| 0.1686        | 79.0  | 19039 | 3.2825          | 79.0246 | 59.1434 | 70.7866 | 70.7353   | 16.3559 |
| 0.1686        | 80.0  | 19280 | 3.2816          | 79.0431 | 58.9944 | 70.6819 | 70.6257   | 16.3486 |
| 0.1664        | 81.0  | 19521 | 3.2567          | 79.0287 | 59.0805 | 70.7245 | 70.6889   | 16.3392 |
| 0.1664        | 82.0  | 19762 | 3.2750          | 78.9783 | 59.1123 | 70.6961 | 70.6369   | 16.3465 |
| 0.1659        | 83.0  | 20003 | 3.2757          | 78.9774 | 59.2248 | 70.7978 | 70.7407   | 16.3455 |
| 0.1659        | 84.0  | 20244 | 3.2800          | 79.0205 | 59.2371 | 70.8016 | 70.7532   | 16.3704 |
| 0.1659        | 85.0  | 20485 | 3.2723          | 78.9507 | 59.1004 | 70.6419 | 70.5825   | 16.3663 |
| 0.165         | 86.0  | 20726 | 3.2723          | 78.9698 | 59.1362 | 70.7738 | 70.7018   | 16.3652 |
| 0.165         | 87.0  | 20967 | 3.2740          | 79.0156 | 59.2383 | 70.7799 | 70.7075   | 16.384  |
| 0.1637        | 88.0  | 21208 | 3.2628          | 79.0806 | 59.3468 | 70.8158 | 70.753    | 16.3725 |
| 0.1637        | 89.0  | 21449 | 3.2605          | 78.9712 | 59.223  | 70.7653 | 70.7021   | 16.3621 |
| 0.1642        | 90.0  | 21690 | 3.2738          | 79.0793 | 59.3309 | 70.8753 | 70.8282   | 16.385  |
| 0.1642        | 91.0  | 21931 | 3.2674          | 78.9877 | 59.1468 | 70.7031 | 70.6598   | 16.3694 |
| 0.1636        | 92.0  | 22172 | 3.2706          | 79.006  | 59.1366 | 70.7069 | 70.6638   | 16.3757 |
| 0.1636        | 93.0  | 22413 | 3.2783          | 79.0874 | 59.2523 | 70.7881 | 70.7397   | 16.3746 |
| 0.1629        | 94.0  | 22654 | 3.2801          | 79.1126 | 59.2826 | 70.8352 | 70.7865   | 16.3715 |
| 0.1629        | 95.0  | 22895 | 3.2797          | 79.0586 | 59.3089 | 70.8126 | 70.7697   | 16.3757 |
| 0.1625        | 96.0  | 23136 | 3.2826          | 79.1019 | 59.3294 | 70.8399 | 70.803    | 16.3777 |
| 0.1625        | 97.0  | 23377 | 3.2844          | 79.0739 | 59.3167 | 70.9062 | 70.846    | 16.3767 |
| 0.1622        | 98.0  | 23618 | 3.2826          | 79.0739 | 59.3167 | 70.9062 | 70.846    | 16.3767 |
| 0.1622        | 99.0  | 23859 | 3.2830          | 79.0721 | 59.355  | 70.9787 | 70.9177   | 16.3819 |
| 0.161         | 100.0 | 24100 | 3.2833          | 79.0721 | 59.355  | 70.9787 | 70.9177   | 16.3819 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1

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

outputs
=======


This model is a fine-tuned version of mrm8488/t5-base-finetuned-common\_gen on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 3.2833
* Rouge1: 79.0721
* Rouge2: 59.355
* Rougel: 70.9787
* Rougelsum: 70.9177
* Gen Len: 16.3819


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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* 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 4.20.1
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1