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# BERT_Tweet_Sentiment_100k_2eps

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1259
- Train Accuracy: 0.9542
- Validation Loss: 0.6133
- Validation Accuracy: 0.8315
- Epoch: 1

## 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': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3330     | 0.8562         | 0.3847          | 0.8415              | 0     |
| 0.1259     | 0.9542         | 0.6133          | 0.8315              | 1     |


### Framework versions

- Transformers 4.16.2
- TensorFlow 2.8.0
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "BERT_Tweet_Sentiment_100k_2eps", "results": []}]}
transformers tf bert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

BERT\_Tweet\_Sentiment\_100k\_2eps
==================================


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


* Train Loss: 0.1259
* Train Accuracy: 0.9542
* Validation Loss: 0.6133
* Validation Accuracy: 0.8315
* Epoch: 1


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': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.16.2
* TensorFlow 2.8.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.16.2
* TensorFlow 2.8.0
* Tokenizers 0.11.0
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.16.2
* TensorFlow 2.8.0
* Tokenizers 0.11.0
passage: TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'clipnorm': 1.0, 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.16.2
* TensorFlow 2.8.0
* Tokenizers 0.11.0