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# keras-io/sentiment-analysis

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6865
- Validation Loss: 0.7002
- Train Accuracy: 0.4908
- Epoch: 4

## 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': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6865     | 0.6975          | 0.4908         | 0     |
| 0.6865     | 0.6973          | 0.4908         | 1     |
| 0.6865     | 0.6976          | 0.4908         | 2     |
| 0.6865     | 0.6975          | 0.4908         | 3     |
| 0.6865     | 0.7002          | 0.4908         | 4     |


### Framework versions

- Transformers 4.16.2
- TensorFlow 2.7.0
- Datasets 1.18.2
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "keras-io/sentiment-analysis", "results": []}]}
transformers tf tensorboard distilbert text-classification generated_from_keras_callback base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

keras-io/sentiment-analysis
===========================


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


* Train Loss: 0.6865
* Validation Loss: 0.7002
* Train Accuracy: 0.4908
* Epoch: 4


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': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.16.2
* TensorFlow 2.7.0
* Datasets 1.18.2
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.16.2
* TensorFlow 2.7.0
* Datasets 1.18.2
* Tokenizers 0.11.0
TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.16.2
* TensorFlow 2.7.0
* Datasets 1.18.2
* Tokenizers 0.11.0
passage: TAGS
#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 1e-04, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.16.2
* TensorFlow 2.7.0
* Datasets 1.18.2
* Tokenizers 0.11.0