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# CIS6930_DAAGR_T5_NoEmo

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: 0.3368
- Train Accuracy: 0.9629
- Validation Loss: 0.4438
- Validation Accuracy: 0.9496
- Epoch: 17

## 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': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.5062     | 0.9405         | 0.4590          | 0.9454              | 0     |
| 0.4381     | 0.9479         | 0.4477          | 0.9472              | 1     |
| 0.4249     | 0.9499         | 0.4423          | 0.9481              | 2     |
| 0.4152     | 0.9513         | 0.4386          | 0.9486              | 3     |
| 0.4071     | 0.9525         | 0.4365          | 0.9490              | 4     |
| 0.4000     | 0.9535         | 0.4349          | 0.9493              | 5     |
| 0.3935     | 0.9545         | 0.4338          | 0.9496              | 6     |
| 0.3876     | 0.9553         | 0.4337          | 0.9498              | 7     |
| 0.3816     | 0.9562         | 0.4338          | 0.9498              | 8     |
| 0.3763     | 0.9571         | 0.4343          | 0.9499              | 9     |
| 0.3708     | 0.9578         | 0.4338          | 0.9500              | 10    |
| 0.3657     | 0.9586         | 0.4357          | 0.9498              | 11    |
| 0.3605     | 0.9593         | 0.4355          | 0.9500              | 12    |
| 0.3556     | 0.9601         | 0.4370          | 0.9499              | 13    |
| 0.3507     | 0.9608         | 0.4380          | 0.9499              | 14    |
| 0.3463     | 0.9615         | 0.4397          | 0.9498              | 15    |
| 0.3413     | 0.9622         | 0.4427          | 0.9496              | 16    |
| 0.3368     | 0.9629         | 0.4438          | 0.9496              | 17    |


### Framework versions

- Transformers 4.27.4
- TensorFlow 2.11.0
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "CIS6930_DAAGR_T5_NoEmo", "results": []}]}
transformers tf t5 text2text-generation generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

CIS6930\_DAAGR\_T5\_NoEmo
=========================


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: 0.3368
* Train Accuracy: 0.9629
* Validation Loss: 0.4438
* Validation Accuracy: 0.9496
* Epoch: 17


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': 0.001, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.27.4
* TensorFlow 2.11.0
* Datasets 2.11.0
* 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': 0.001, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.4
* TensorFlow 2.11.0
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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': 0.001, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.4
* TensorFlow 2.11.0
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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': 0.001, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.27.4
* TensorFlow 2.11.0
* Datasets 2.11.0
* Tokenizers 0.13.2