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# LucaReggiani/t5-small-nlpfinalproject3-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: 2.9916
- Validation Loss: 2.9840
- Train Rouge1: 22.3282
- Train Rouge2: 4.7253
- Train Rougel: 17.9286
- Train Rougelsum: 17.9126
- Train Gen Len: 18.54
- Epoch: 7

## 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 |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 3.7453     | 3.2008          | 19.6619      | 3.4413       | 15.6174      | 15.6444         | 18.23         | 0     |
| 3.3973     | 3.1041          | 21.1867      | 4.1818       | 16.5584      | 16.4568         | 18.59         | 1     |
| 3.2886     | 3.0600          | 21.8364      | 4.3416       | 16.6696      | 16.6382         | 18.5          | 2     |
| 3.2216     | 3.0323          | 23.5970      | 5.3080       | 18.4737      | 18.3755         | 18.49         | 3     |
| 3.1462     | 3.0174          | 23.0720      | 5.3486       | 18.5011      | 18.4635         | 18.62         | 4     |
| 3.0860     | 3.0017          | 22.3949      | 4.7088       | 17.7759      | 17.7328         | 18.51         | 5     |
| 3.0436     | 2.9890          | 22.8096      | 4.9911       | 18.1200      | 18.0347         | 18.47         | 6     |
| 2.9916     | 2.9840          | 22.3282      | 4.7253       | 17.9286      | 17.9126         | 18.54         | 7     |


### 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-nlpfinalproject3-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-nlpfinalproject3-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: 2.9916
* Validation Loss: 2.9840
* Train Rouge1: 22.3282
* Train Rouge2: 4.7253
* Train Rougel: 17.9286
* Train Rougelsum: 17.9126
* Train Gen Len: 18.54
* Epoch: 7


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