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# LucaReggiani/t5-small-nlpfinalproject4-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: 3.0688
- Validation Loss: 2.9609
- Train Rouge1: 22.9985
- Train Rouge2: 5.0413
- Train Rougel: 18.1856
- Train Rougelsum: 18.0816
- Train Gen Len: 18.67
- Epoch: 8

## 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 |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.8921     | 3.2708          | 18.8870      | 3.0920       | 14.9668      | 14.9517         | 18.67         | 0     |
| 3.5034     | 3.1209          | 21.5417      | 3.8130       | 16.5211      | 16.5045         | 18.37         | 1     |
| 3.3763     | 3.0605          | 21.0710      | 3.6133       | 15.7808      | 15.7437         | 18.33         | 2     |
| 3.2971     | 3.0305          | 21.6173      | 4.0001       | 16.2502      | 16.2302         | 18.5          | 3     |
| 3.2452     | 3.0086          | 22.8085      | 4.9522       | 17.8831      | 17.7797         | 18.6          | 4     |
| 3.1899     | 2.9920          | 22.7903      | 5.3026       | 17.8844      | 17.8651         | 18.58         | 5     |
| 3.1514     | 2.9775          | 23.0533      | 5.3456       | 18.4312      | 18.3636         | 18.52         | 6     |
| 3.1050     | 2.9686          | 23.0767      | 5.1264       | 18.4552      | 18.3503         | 18.54         | 7     |
| 3.0688     | 2.9609          | 22.9985      | 5.0413       | 18.1856      | 18.0816         | 18.67         | 8     |


### Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "LucaReggiani/t5-small-nlpfinalproject4-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-nlpfinalproject4-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: 3.0688
* Validation Loss: 2.9609
* Train Rouge1: 22.9985
* Train Rouge2: 5.0413
* Train Rougel: 18.1856
* Train Rougelsum: 18.0816
* Train Gen Len: 18.67
* Epoch: 8


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 2.9.0
* 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 2.9.0
* 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 2.9.0
* 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 2.9.0
* Tokenizers 0.13.2