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# LucaReggiani/t5-small-11nlpfinalproject11-xsum

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0366
- Validation Loss: 2.9572
- Train Rouge1: 23.0678
- Train Rouge2: 4.8820
- Train Rougel: 18.2146
- Train Rougelsum: 18.0961
- Train Gen Len: 18.73
- Epoch: 9

## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.8944     | 3.2778          | 18.4040      | 2.9447       | 14.7588      | 14.8854         | 18.71         | 0     |
| 3.5118     | 3.1285          | 21.0571      | 4.0329       | 16.5313      | 16.5872         | 18.17         | 1     |
| 3.3821     | 3.0720          | 21.2823      | 4.1817       | 16.3643      | 16.3809         | 18.38         | 2     |
| 3.3099     | 3.0368          | 21.3656      | 4.0228       | 16.6094      | 16.5866         | 18.5          | 3     |
| 3.2464     | 3.0117          | 21.6946      | 4.2746       | 16.7999      | 16.7907         | 18.68         | 4     |
| 3.2081     | 2.9932          | 23.3785      | 5.3998       | 18.5529      | 18.5770         | 18.6          | 5     |
| 3.1603     | 2.9809          | 23.2570      | 5.4772       | 18.6532      | 18.6172         | 18.55         | 6     |
| 3.1169     | 2.9719          | 23.0897      | 4.7919       | 18.2567      | 18.1743         | 18.59         | 7     |
| 3.0696     | 2.9681          | 22.5213      | 4.9309       | 17.9595      | 17.8530         | 18.6          | 8     |
| 3.0366     | 2.9572          | 23.0678      | 4.8820       | 18.2146      | 18.0961         | 18.73         | 9     |


### Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "LucaReggiani/t5-small-11nlpfinalproject11-xsum", "results": []}]}
transformers tf t5 text2text-generation generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

LucaReggiani/t5-small-11nlpfinalproject11-xsum
==============================================


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


* Train Loss: 3.0366
* Validation Loss: 2.9572
* Train Rouge1: 23.0678
* Train Rouge2: 4.8820
* Train Rougel: 18.2146
* Train Rougelsum: 18.0961
* Train Gen Len: 18.73
* Epoch: 9


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:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 5e-05, 'beta\_1': 0.9, 'beta\_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.26.1
* TensorFlow 2.11.0
* Datasets 2.10.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 5e-05, 'beta\_1': 0.9, 'beta\_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.1
* TensorFlow 2.11.0
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 5e-05, 'beta\_1': 0.9, 'beta\_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.1
* TensorFlow 2.11.0
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': 5e-05, 'beta\_1': 0.9, 'beta\_2': 0.98, 'epsilon': 1e-06, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.26.1
* TensorFlow 2.11.0
* Datasets 2.10.0
* Tokenizers 0.13.2