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# LucaReggiani/t5-small-nlpfinalproject55-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: 3.1996
- Validation Loss: 3.0509
- Train Rouge1: 21.8691
- Train Rouge2: 5.0380
- Train Rougel: 17.3737
- Train Rougelsum: 17.3666
- Train Gen Len: 18.62
- 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': 'SGD', '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': 0.01, 'momentum': 0.9, 'nesterov': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.8269     | 3.3077          | 18.3354      | 2.6346       | 14.6819      | 14.5948         | 18.56         | 0     |
| 3.6784     | 3.2525          | 20.0886      | 2.8244       | 15.8006      | 15.8320         | 17.89         | 1     |
| 3.5221     | 3.1223          | 20.4536      | 3.7761       | 15.5971      | 15.5748         | 18.5          | 2     |
| 3.3981     | 3.1003          | 22.0145      | 4.8444       | 17.1468      | 17.1329         | 18.01         | 3     |
| 3.3380     | 3.0591          | 21.8603      | 4.4109       | 16.9049      | 16.9147         | 18.45         | 4     |
| 3.3200     | 3.1147          | 21.5178      | 4.7155       | 17.4288      | 17.4466         | 18.78         | 5     |
| 3.3745     | 3.0972          | 22.3848      | 4.4307       | 17.2203      | 17.2453         | 18.14         | 6     |
| 3.2990     | 3.0936          | 23.0953      | 4.6457       | 18.0523      | 18.0075         | 17.73         | 7     |
| 3.2382     | 3.0578          | 23.0047      | 5.2161       | 18.0230      | 18.0116         | 18.46         | 8     |
| 3.1996     | 3.0509          | 21.8691      | 5.0380       | 17.3737      | 17.3666         | 18.62         | 9     |


### 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-nlpfinalproject55-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-nlpfinalproject55-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: 3.1996
* Validation Loss: 3.0509
* Train Rouge1: 21.8691
* Train Rouge2: 5.0380
* Train Rougel: 17.3737
* Train Rougelsum: 17.3666
* Train Gen Len: 18.62
* 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': 'SGD', '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': 0.01, 'momentum': 0.9, 'nesterov': 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': 'SGD', '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': 0.01, 'momentum': 0.9, 'nesterov': 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': 'SGD', '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': 0.01, 'momentum': 0.9, 'nesterov': 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': 'SGD', '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': 0.01, 'momentum': 0.9, 'nesterov': False}
* training\_precision: float32### Training results### Framework versions


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