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# FYP2022

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6016
- Train Sparse Categorical Accuracy: 0.7503
- Train Sparse Top 3 Categorical Accuracy: 0.9901
- Epoch: 5

## 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', 'clipnorm': 1.0, 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Sparse Categorical Accuracy | Train Sparse Top 3 Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------------------------------:|:-----:|
| 0.9433     | 0.5975                            | 0.9523                                  | 0     |
| 0.8257     | 0.6498                            | 0.9704                                  | 1     |
| 0.7625     | 0.6765                            | 0.9778                                  | 2     |
| 0.7062     | 0.7014                            | 0.9832                                  | 3     |
| 0.6526     | 0.7263                            | 0.9872                                  | 4     |
| 0.6016     | 0.7503                            | 0.9901                                  | 5     |


### Framework versions

- Transformers 4.19.2
- TensorFlow 2.8.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "FYP2022", "results": []}]}
transformers tf roberta text-classification generated_from_keras_callback license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #roberta #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 

FYP2022
=======


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


* Train Loss: 0.6016
* Train Sparse Categorical Accuracy: 0.7503
* Train Sparse Top 3 Categorical Accuracy: 0.9901
* Epoch: 5


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', 'clipnorm': 1.0, 'learning\_rate': 1e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.19.2
* TensorFlow 2.8.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\_rate': 1e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.19.2
* TensorFlow 2.8.0
* Tokenizers 0.12.1
TAGS
#transformers #tf #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', 'clipnorm': 1.0, 'learning\_rate': 1e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.19.2
* TensorFlow 2.8.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #tf #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', 'clipnorm': 1.0, 'learning\_rate': 1e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.19.2
* TensorFlow 2.8.0
* Tokenizers 0.12.1