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# LucaReggiani/t5-small-nlpfinalproject9-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: 3.2913
- Validation Loss: 3.0455
- Train Rouge1: 20.7408
- Train Rouge2: 3.7581
- Train Rougel: 15.8941
- Train Rougelsum: 15.9289
- Train Gen Len: 18.55
- 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': 'AdamWeightDecay', 'learning_rate': 1.9e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.1}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 4.1156     | 3.6725          | 17.2479      | 2.3397       | 13.5168      | 13.5519         | 19.0          | 0     |
| 3.7704     | 3.3823          | 17.8969      | 2.4369       | 14.0233      | 14.0407         | 18.87         | 1     |
| 3.6193     | 3.2493          | 18.6977      | 3.1336       | 15.2562      | 15.3566         | 18.61         | 2     |
| 3.5278     | 3.1862          | 19.2581      | 3.2722       | 15.4920      | 15.5343         | 18.4          | 3     |
| 3.4656     | 3.1441          | 20.4869      | 3.7535       | 16.0946      | 16.1638         | 18.29         | 4     |
| 3.4119     | 3.1149          | 21.3587      | 4.0175       | 16.4030      | 16.4600         | 18.38         | 5     |
| 3.3767     | 3.0909          | 21.0503      | 3.8763       | 16.2466      | 16.2965         | 18.37         | 6     |
| 3.3529     | 3.0731          | 20.9553      | 4.0283       | 16.5227      | 16.5765         | 18.36         | 7     |
| 3.3183     | 3.0572          | 20.8371      | 3.6678       | 16.3675      | 16.3987         | 18.5          | 8     |
| 3.2913     | 3.0455          | 20.7408      | 3.7581       | 15.8941      | 15.9289         | 18.55         | 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-nlpfinalproject9-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-nlpfinalproject9-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: 3.2913
* Validation Loss: 3.0455
* Train Rouge1: 20.7408
* Train Rouge2: 3.7581
* Train Rougel: 15.8941
* Train Rougelsum: 15.9289
* Train Gen Len: 18.55
* 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': 'AdamWeightDecay', 'learning\_rate': 1.9e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.1}
* 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': 'AdamWeightDecay', 'learning\_rate': 1.9e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.1}
* 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': 'AdamWeightDecay', 'learning\_rate': 1.9e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.1}
* 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': 'AdamWeightDecay', 'learning\_rate': 1.9e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.1}
* training\_precision: float32### Training results### Framework versions


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