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# cptanalatriste/request-for-help

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: 0.1342
- Train Sparse Categorical Accuracy: 1.0
- Validation Loss: 0.1514
- Validation Sparse Categorical Accuracy: 0.9796
- Epoch: 19

## 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': 3e-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 | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.8291     | 0.375                             | 0.7483          | 0.3673                                 | 0     |
| 0.7470     | 0.375                             | 0.6302          | 0.8163                                 | 1     |
| 0.6504     | 0.625                             | 0.6079          | 0.9184                                 | 2     |
| 0.6128     | 0.7812                            | 0.5882          | 0.8980                                 | 3     |
| 0.5939     | 0.8125                            | 0.5639          | 0.9184                                 | 4     |
| 0.5300     | 0.9688                            | 0.5378          | 0.9184                                 | 5     |
| 0.5306     | 0.9688                            | 0.5098          | 0.9388                                 | 6     |
| 0.4963     | 1.0                               | 0.4806          | 0.9388                                 | 7     |
| 0.4683     | 0.9688                            | 0.4434          | 0.9592                                 | 8     |
| 0.3959     | 1.0                               | 0.4070          | 0.9796                                 | 9     |
| 0.3807     | 1.0                               | 0.3762          | 0.9796                                 | 10    |
| 0.3509     | 1.0                               | 0.3439          | 0.9796                                 | 11    |
| 0.3013     | 1.0                               | 0.3064          | 0.9796                                 | 12    |
| 0.2848     | 1.0                               | 0.2931          | 0.9796                                 | 13    |
| 0.2587     | 1.0                               | 0.2681          | 0.9796                                 | 14    |
| 0.2510     | 1.0                               | 0.2295          | 0.9796                                 | 15    |
| 0.1867     | 1.0                               | 0.2000          | 0.9796                                 | 16    |
| 0.1652     | 1.0                               | 0.1793          | 0.9796                                 | 17    |
| 0.1297     | 1.0                               | 0.1637          | 0.9796                                 | 18    |
| 0.1342     | 1.0                               | 0.1514          | 0.9796                                 | 19    |


### Framework versions

- Transformers 4.17.0
- TensorFlow 2.6.2
- Datasets 1.18.4
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "cptanalatriste/request-for-help", "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 

cptanalatriste/request-for-help
===============================


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: 0.1342
* Train Sparse Categorical Accuracy: 1.0
* Validation Loss: 0.1514
* Validation Sparse Categorical Accuracy: 0.9796
* Epoch: 19


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': 3e-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 4.17.0
* TensorFlow 2.6.2
* Datasets 1.18.4
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 3e-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 4.17.0
* TensorFlow 2.6.2
* Datasets 1.18.4
* Tokenizers 0.11.6
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', 'learning\_rate': 3e-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 4.17.0
* TensorFlow 2.6.2
* Datasets 1.18.4
* Tokenizers 0.11.6
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', 'learning\_rate': 3e-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 4.17.0
* TensorFlow 2.6.2
* Datasets 1.18.4
* Tokenizers 0.11.6