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# summarization_finetuned

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5478
- Validation Loss: 1.4195
- Train Rougel: tf.Tensor(0.29894578, shape=(), dtype=float32)
- Epoch: 0

## 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': 'Adamax', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Rougel                                   | Epoch |
|:----------:|:---------------:|:----------------------------------------------:|:-----:|
| 1.5478     | 1.4195          | tf.Tensor(0.29894578, shape=(), dtype=float32) | 0     |


### Framework versions

- Transformers 4.25.1
- TensorFlow 2.10.0
- Datasets 2.6.1
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "summarization_finetuned", "results": []}]}
transformers tf bart text2text-generation generated_from_keras_callback license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 

summarization\_finetuned
========================


This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 1.5478
* Validation Loss: 1.4195
* Train Rougel: tf.Tensor(0.29894578, shape=(), dtype=float32)
* Epoch: 0


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': 'Adamax', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.25.1
* TensorFlow 2.10.0
* Datasets 2.6.1
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adamax', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.25.1
* TensorFlow 2.10.0
* Datasets 2.6.1
* Tokenizers 0.12.1
TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adamax', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.25.1
* TensorFlow 2.10.0
* Datasets 2.6.1
* Tokenizers 0.12.1
TAGS
#transformers #tf #bart #text2text-generation #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adamax', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.25.1
* TensorFlow 2.10.0
* Datasets 2.6.1
* Tokenizers 0.12.1