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# huynhdoo/distilcamembert-base-finetuned-jva-missions-report

This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0336
- Validation Loss: 1.1880
- Train F1: 0.0391
- Epoch: 17

## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train F1 | Epoch |
|:----------:|:---------------:|:--------:|:-----:|
| 0.5225     | 0.4756          | 0.3575   | 0     |
| 0.4079     | 0.4294          | 0.2961   | 1     |
| 0.3439     | 0.5053          | 0.2961   | 2     |
| 0.2765     | 0.5106          | 0.2346   | 3     |
| 0.2044     | 0.5352          | 0.1788   | 4     |
| 0.1774     | 0.6706          | 0.1341   | 5     |
| 0.1690     | 0.8693          | 0.1676   | 6     |
| 0.1143     | 0.7711          | 0.0726   | 7     |
| 0.0930     | 0.9906          | 0.0950   | 8     |
| 0.1091     | 0.9093          | 0.1117   | 9     |
| 0.0576     | 0.8518          | 0.0894   | 10    |
| 0.0500     | 1.2538          | 0.0950   | 11    |
| 0.0541     | 0.7193          | 0.0838   | 12    |
| 0.0461     | 0.9906          | 0.0503   | 13    |
| 0.0359     | 0.9036          | 0.0447   | 14    |
| 0.0320     | 1.1648          | 0.0391   | 15    |
| 0.0299     | 1.0017          | 0.0279   | 16    |
| 0.0336     | 1.1880          | 0.0391   | 17    |


### Framework versions

- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "huynhdoo/distilcamembert-base-finetuned-jva-missions-report", "results": []}]}
transformers tf tensorboard camembert text-classification generated_from_keras_callback license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #tensorboard #camembert #text-classification #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us 

huynhdoo/distilcamembert-base-finetuned-jva-missions-report
===========================================================


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


* Train Loss: 0.0336
* Validation Loss: 1.1880
* Train F1: 0.0391
* Epoch: 17


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': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 3000, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 3000, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #tensorboard #camembert #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', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 3000, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #tensorboard #camembert #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', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 3000, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Datasets 2.9.0
* Tokenizers 0.13.2