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# vjsyong/xlm-roberta-dementia_detection

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0140
- Validation Loss: 0.4773
- Train Accuracy: 0.8958
- Epoch: 13

## 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 378, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1762     | 0.3984          | 0.875          | 0     |
| 0.1873     | 0.3250          | 0.8542         | 1     |
| 0.1339     | 0.4448          | 0.875          | 2     |
| 0.0316     | 0.4015          | 0.8958         | 3     |
| 0.0226     | 0.4410          | 0.875          | 4     |
| 0.0166     | 0.4586          | 0.8958         | 5     |
| 0.0157     | 0.4710          | 0.8958         | 6     |
| 0.0113     | 0.4772          | 0.8958         | 7     |
| 0.0159     | 0.4773          | 0.8958         | 8     |
| 0.0105     | 0.4773          | 0.8958         | 9     |
| 0.0119     | 0.4773          | 0.8958         | 10    |
| 0.0120     | 0.4773          | 0.8958         | 11    |
| 0.0135     | 0.4773          | 0.8958         | 12    |
| 0.0140     | 0.4773          | 0.8958         | 13    |


### Framework versions

- Transformers 4.28.1
- TensorFlow 2.10.1
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "vjsyong/xlm-roberta-dementia_detection", "results": []}]}
transformers tf xlm-roberta text-classification generated_from_keras_callback license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #xlm-roberta #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 

vjsyong/xlm-roberta-dementia\_detection
=======================================


This model is a fine-tuned version of xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Train Loss: 0.0140
* Validation Loss: 0.4773
* Train Accuracy: 0.8958
* Epoch: 13


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', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 378, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.28.1
* TensorFlow 2.10.1
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 378, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.10.1
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #xlm-roberta #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 378, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.28.1
* TensorFlow 2.10.1
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #tf #xlm-roberta #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 378, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.28.1
* TensorFlow 2.10.1
* Datasets 2.11.0
* Tokenizers 0.13.3