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# t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2

This model is a fine-tuned version of [anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol](https://huggingface.co/anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0069
- Bleu: 28.1311
- Gen Len: 18.7412

## 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: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log        | 1.0   | 22   | 0.0692          | 27.4908 | 18.7353 |
| No log        | 2.0   | 44   | 0.0631          | 27.554  | 18.7294 |
| No log        | 3.0   | 66   | 0.0533          | 27.6007 | 18.7294 |
| No log        | 4.0   | 88   | 0.0484          | 27.6446 | 18.7294 |
| No log        | 5.0   | 110  | 0.0439          | 27.6401 | 18.7294 |
| No log        | 6.0   | 132  | 0.0404          | 27.5117 | 18.7294 |
| No log        | 7.0   | 154  | 0.0389          | 27.6358 | 18.7294 |
| No log        | 8.0   | 176  | 0.0362          | 27.6358 | 18.7294 |
| No log        | 9.0   | 198  | 0.0339          | 27.5731 | 18.7294 |
| No log        | 10.0  | 220  | 0.0319          | 27.2326 | 18.6882 |
| No log        | 11.0  | 242  | 0.0298          | 27.2326 | 18.6882 |
| No log        | 12.0  | 264  | 0.0293          | 27.5498 | 18.7294 |
| No log        | 13.0  | 286  | 0.0276          | 27.6566 | 18.7294 |
| No log        | 14.0  | 308  | 0.0268          | 27.6566 | 18.7294 |
| No log        | 15.0  | 330  | 0.0251          | 27.6107 | 18.7294 |
| No log        | 16.0  | 352  | 0.0239          | 27.7096 | 18.7294 |
| No log        | 17.0  | 374  | 0.0228          | 27.6716 | 18.7294 |
| No log        | 18.0  | 396  | 0.0231          | 27.8083 | 18.7294 |
| No log        | 19.0  | 418  | 0.0218          | 27.4838 | 18.6882 |
| No log        | 20.0  | 440  | 0.0212          | 27.4712 | 18.6882 |
| No log        | 21.0  | 462  | 0.0197          | 27.8787 | 18.7353 |
| No log        | 22.0  | 484  | 0.0207          | 27.6899 | 18.6941 |
| 0.1026        | 23.0  | 506  | 0.0186          | 27.6376 | 18.6941 |
| 0.1026        | 24.0  | 528  | 0.0202          | 27.6672 | 18.6941 |
| 0.1026        | 25.0  | 550  | 0.0174          | 28.0172 | 18.7412 |
| 0.1026        | 26.0  | 572  | 0.0170          | 27.8714 | 18.7412 |
| 0.1026        | 27.0  | 594  | 0.0164          | 27.7423 | 18.7412 |
| 0.1026        | 28.0  | 616  | 0.0164          | 27.8278 | 18.7412 |
| 0.1026        | 29.0  | 638  | 0.0163          | 27.8278 | 18.7412 |
| 0.1026        | 30.0  | 660  | 0.0158          | 27.907  | 18.7412 |
| 0.1026        | 31.0  | 682  | 0.0165          | 27.7752 | 18.7412 |
| 0.1026        | 32.0  | 704  | 0.0147          | 27.8284 | 18.7412 |
| 0.1026        | 33.0  | 726  | 0.0150          | 27.8862 | 18.7412 |
| 0.1026        | 34.0  | 748  | 0.0148          | 27.8402 | 18.7412 |
| 0.1026        | 35.0  | 770  | 0.0141          | 27.8353 | 18.7412 |
| 0.1026        | 36.0  | 792  | 0.0142          | 27.858  | 18.7412 |
| 0.1026        | 37.0  | 814  | 0.0143          | 27.858  | 18.7412 |
| 0.1026        | 38.0  | 836  | 0.0158          | 27.8353 | 18.7412 |
| 0.1026        | 39.0  | 858  | 0.0125          | 27.8913 | 18.7412 |
| 0.1026        | 40.0  | 880  | 0.0121          | 27.9167 | 18.7412 |
| 0.1026        | 41.0  | 902  | 0.0122          | 27.9569 | 18.7412 |
| 0.1026        | 42.0  | 924  | 0.0126          | 27.9569 | 18.7412 |
| 0.1026        | 43.0  | 946  | 0.0120          | 28.001  | 18.7412 |
| 0.1026        | 44.0  | 968  | 0.0125          | 28.0079 | 18.7412 |
| 0.1026        | 45.0  | 990  | 0.0115          | 28.0079 | 18.7412 |
| 0.072         | 46.0  | 1012 | 0.0113          | 27.9851 | 18.7412 |
| 0.072         | 47.0  | 1034 | 0.0113          | 28.0184 | 18.7412 |
| 0.072         | 48.0  | 1056 | 0.0110          | 28.0184 | 18.7412 |
| 0.072         | 49.0  | 1078 | 0.0108          | 28.0184 | 18.7412 |
| 0.072         | 50.0  | 1100 | 0.0107          | 28.0184 | 18.7412 |
| 0.072         | 51.0  | 1122 | 0.0101          | 28.0184 | 18.7412 |
| 0.072         | 52.0  | 1144 | 0.0102          | 28.0184 | 18.7412 |
| 0.072         | 53.0  | 1166 | 0.0099          | 28.0184 | 18.7412 |
| 0.072         | 54.0  | 1188 | 0.0100          | 28.0184 | 18.7412 |
| 0.072         | 55.0  | 1210 | 0.0102          | 28.0184 | 18.7412 |
| 0.072         | 56.0  | 1232 | 0.0095          | 28.0184 | 18.7412 |
| 0.072         | 57.0  | 1254 | 0.0098          | 28.0184 | 18.7412 |
| 0.072         | 58.0  | 1276 | 0.0092          | 28.0184 | 18.7412 |
| 0.072         | 59.0  | 1298 | 0.0090          | 28.0184 | 18.7412 |
| 0.072         | 60.0  | 1320 | 0.0095          | 28.0184 | 18.7412 |
| 0.072         | 61.0  | 1342 | 0.0092          | 27.9674 | 18.7412 |
| 0.072         | 62.0  | 1364 | 0.0091          | 27.9419 | 18.7412 |
| 0.072         | 63.0  | 1386 | 0.0100          | 27.9419 | 18.7412 |
| 0.072         | 64.0  | 1408 | 0.0084          | 28.0752 | 18.7412 |
| 0.072         | 65.0  | 1430 | 0.0086          | 28.0192 | 18.7412 |
| 0.072         | 66.0  | 1452 | 0.0084          | 28.0192 | 18.7412 |
| 0.072         | 67.0  | 1474 | 0.0085          | 28.0192 | 18.7412 |
| 0.072         | 68.0  | 1496 | 0.0087          | 28.0192 | 18.7412 |
| 0.0575        | 69.0  | 1518 | 0.0084          | 28.0192 | 18.7412 |
| 0.0575        | 70.0  | 1540 | 0.0080          | 28.0192 | 18.7412 |
| 0.0575        | 71.0  | 1562 | 0.0082          | 28.0192 | 18.7412 |
| 0.0575        | 72.0  | 1584 | 0.0080          | 28.0192 | 18.7412 |
| 0.0575        | 73.0  | 1606 | 0.0075          | 28.0192 | 18.7412 |
| 0.0575        | 74.0  | 1628 | 0.0079          | 28.0192 | 18.7412 |
| 0.0575        | 75.0  | 1650 | 0.0078          | 28.0752 | 18.7412 |
| 0.0575        | 76.0  | 1672 | 0.0076          | 28.1311 | 18.7412 |
| 0.0575        | 77.0  | 1694 | 0.0073          | 28.1311 | 18.7412 |
| 0.0575        | 78.0  | 1716 | 0.0074          | 28.1311 | 18.7412 |
| 0.0575        | 79.0  | 1738 | 0.0072          | 28.1311 | 18.7412 |
| 0.0575        | 80.0  | 1760 | 0.0078          | 28.1311 | 18.7412 |
| 0.0575        | 81.0  | 1782 | 0.0077          | 28.1311 | 18.7412 |
| 0.0575        | 82.0  | 1804 | 0.0071          | 28.1311 | 18.7412 |
| 0.0575        | 83.0  | 1826 | 0.0072          | 28.1311 | 18.7412 |
| 0.0575        | 84.0  | 1848 | 0.0075          | 28.1311 | 18.7412 |
| 0.0575        | 85.0  | 1870 | 0.0071          | 28.1311 | 18.7412 |
| 0.0575        | 86.0  | 1892 | 0.0070          | 28.1311 | 18.7412 |
| 0.0575        | 87.0  | 1914 | 0.0069          | 28.1311 | 18.7412 |
| 0.0575        | 88.0  | 1936 | 0.0069          | 28.1311 | 18.7412 |
| 0.0575        | 89.0  | 1958 | 0.0069          | 28.1311 | 18.7412 |
| 0.0575        | 90.0  | 1980 | 0.0069          | 28.1311 | 18.7412 |
| 0.0509        | 91.0  | 2002 | 0.0069          | 28.1311 | 18.7412 |
| 0.0509        | 92.0  | 2024 | 0.0070          | 28.1311 | 18.7412 |
| 0.0509        | 93.0  | 2046 | 0.0069          | 28.1311 | 18.7412 |
| 0.0509        | 94.0  | 2068 | 0.0070          | 28.1311 | 18.7412 |
| 0.0509        | 95.0  | 2090 | 0.0069          | 28.1311 | 18.7412 |
| 0.0509        | 96.0  | 2112 | 0.0069          | 28.1311 | 18.7412 |
| 0.0509        | 97.0  | 2134 | 0.0069          | 28.1311 | 18.7412 |
| 0.0509        | 98.0  | 2156 | 0.0069          | 28.1311 | 18.7412 |
| 0.0509        | 99.0  | 2178 | 0.0069          | 28.1311 | 18.7412 |
| 0.0509        | 100.0 | 2200 | 0.0069          | 28.1311 | 18.7412 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2
================================================================================================


This model is a fine-tuned version of anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0069
* Bleu: 28.1311
* Gen Len: 18.7412


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: linear
* num\_epochs: 100
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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: linear
* num\_epochs: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #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: linear
* num\_epochs: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #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: linear
* num\_epochs: 100
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1