
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# cruiser/twitter_roberta_final_model

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0648
- Validation Loss: 1.0107
- Train Accuracy: 0.7943
- 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': '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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 1e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 34090, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 250, 'power': 1.0, 'name': None}}, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.5482     | 0.4911          | 0.7991         | 0     |
| 0.4389     | 0.5053          | 0.7972         | 1     |
| 0.3567     | 0.5357          | 0.7935         | 2     |
| 0.2774     | 0.6193          | 0.7872         | 3     |
| 0.2080     | 0.6732          | 0.7989         | 4     |
| 0.1545     | 0.7639          | 0.7889         | 5     |
| 0.1162     | 0.8836          | 0.7855         | 6     |
| 0.0943     | 0.9301          | 0.7903         | 7     |
| 0.0768     | 0.9647          | 0.7929         | 8     |
| 0.0648     | 1.0107          | 0.7943         | 9     |


### Framework versions

- Transformers 4.27.4
- TensorFlow 2.11.0
- Datasets 2.1.0
- Tokenizers 0.13.2

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

cruiser/twitter\_roberta\_final\_model
======================================


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


* Train Loss: 0.0648
* Validation Loss: 1.0107
* Train Accuracy: 0.7943
* 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': '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': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 34090, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 250, 'power': 1.0, 'name': None}}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.27.4
* TensorFlow 2.11.0
* Datasets 2.1.0
* 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': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 34090, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 250, 'power': 1.0, 'name': None}}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.4
* TensorFlow 2.11.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #xlm-roberta #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #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': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 1e-05, 'decay\_steps': 34090, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 250, 'power': 1.0, 'name': None}}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.27.4
* TensorFlow 2.11.0
* Datasets 2.1.0
* Tokenizers 0.13.2