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# t5-test2sql

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Rouge2 Precision: 0.9214
- Rouge2 Recall: 0.4259
- Rouge2 Fmeasure: 0.5578

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| No log        | 1.0   | 11   | 2.7293          | 0.1012           | 0.0305        | 0.0453          |
| No log        | 2.0   | 22   | 1.9009          | 0.0937           | 0.0292        | 0.0427          |
| No log        | 3.0   | 33   | 1.3525          | 0.1002           | 0.0349        | 0.0502          |
| No log        | 4.0   | 44   | 0.8837          | 0.1462           | 0.0529        | 0.0744          |
| No log        | 5.0   | 55   | 0.6460          | 0.5546           | 0.2531        | 0.3371          |
| No log        | 6.0   | 66   | 0.5050          | 0.729            | 0.3571        | 0.4631          |
| No log        | 7.0   | 77   | 0.4239          | 0.6944           | 0.3048        | 0.4088          |
| No log        | 8.0   | 88   | 0.3799          | 0.7868           | 0.3674        | 0.4807          |
| No log        | 9.0   | 99   | 0.3405          | 0.7266           | 0.3126        | 0.4213          |
| No log        | 10.0  | 110  | 0.3055          | 0.8447           | 0.3876        | 0.5104          |
| No log        | 11.0  | 121  | 0.2741          | 0.8546           | 0.3955        | 0.5201          |
| No log        | 12.0  | 132  | 0.2605          | 0.8676           | 0.4049        | 0.5308          |
| No log        | 13.0  | 143  | 0.2446          | 0.8424           | 0.3814        | 0.5047          |
| No log        | 14.0  | 154  | 0.2287          | 0.8659           | 0.3945        | 0.5238          |
| No log        | 15.0  | 165  | 0.2209          | 0.9064           | 0.4273        | 0.556           |
| No log        | 16.0  | 176  | 0.1990          | 0.888            | 0.409         | 0.5383          |
| No log        | 17.0  | 187  | 0.1941          | 0.9118           | 0.4305        | 0.5602          |
| No log        | 18.0  | 198  | 0.1785          | 0.9118           | 0.4305        | 0.5602          |
| No log        | 19.0  | 209  | 0.1669          | 0.919            | 0.4324        | 0.5636          |
| No log        | 20.0  | 220  | 0.1749          | 0.9138           | 0.4289        | 0.5608          |
| No log        | 21.0  | 231  | 0.1598          | 0.9047           | 0.4248        | 0.556           |
| No log        | 22.0  | 242  | 0.1501          | 0.9098           | 0.4294        | 0.5596          |
| No log        | 23.0  | 253  | 0.1456          | 0.9138           | 0.4307        | 0.5618          |
| No log        | 24.0  | 264  | 0.1419          | 0.893            | 0.4185        | 0.5467          |
| No log        | 25.0  | 275  | 0.1359          | 0.9005           | 0.4212        | 0.55            |
| No log        | 26.0  | 286  | 0.1338          | 0.8979           | 0.4212        | 0.5494          |
| No log        | 27.0  | 297  | 0.1319          | 0.9005           | 0.4212        | 0.55            |
| No log        | 28.0  | 308  | 0.1325          | 0.9005           | 0.4212        | 0.55            |
| No log        | 29.0  | 319  | 0.1335          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 30.0  | 330  | 0.1240          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 31.0  | 341  | 0.1222          | 0.9053           | 0.4231        | 0.5527          |
| No log        | 32.0  | 352  | 0.1265          | 0.9214           | 0.4259        | 0.5578          |
| No log        | 33.0  | 363  | 0.1286          | 0.9214           | 0.4259        | 0.5578          |
| No log        | 34.0  | 374  | 0.1283          | 0.9214           | 0.4259        | 0.5578          |
| No log        | 35.0  | 385  | 0.1279          | 0.9214           | 0.4259        | 0.5578          |
| No log        | 36.0  | 396  | 0.1285          | 0.9214           | 0.4259        | 0.5578          |
| No log        | 37.0  | 407  | 0.1291          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 38.0  | 418  | 0.1270          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 39.0  | 429  | 0.1225          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 40.0  | 440  | 0.1205          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 41.0  | 451  | 0.1210          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 42.0  | 462  | 0.1230          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 43.0  | 473  | 0.1250          | 0.9093           | 0.4231        | 0.5529          |
| No log        | 44.0  | 484  | 0.1223          | 0.9214           | 0.4259        | 0.5578          |
| No log        | 45.0  | 495  | 0.1226          | 0.9214           | 0.4259        | 0.5578          |
| 0.5006        | 46.0  | 506  | 0.1213          | 0.9214           | 0.4259        | 0.5578          |
| 0.5006        | 47.0  | 517  | 0.1205          | 0.9214           | 0.4259        | 0.5578          |
| 0.5006        | 48.0  | 528  | 0.1203          | 0.9214           | 0.4259        | 0.5578          |
| 0.5006        | 49.0  | 539  | 0.1206          | 0.9214           | 0.4259        | 0.5578          |
| 0.5006        | 50.0  | 550  | 0.1207          | 0.9214           | 0.4259        | 0.5578          |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-test2sql", "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-test2sql
===========


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


* Loss: 0.1207
* Rouge2 Precision: 0.9214
* Rouge2 Recall: 0.4259
* Rouge2 Fmeasure: 0.5578


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: 50


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* 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: 50 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* 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: 50 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* 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: 50### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1