---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: huynhdoo/distilcamembert-base-finetuned-jva-missions-report
  results: []
---

<!-- 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. -->

# 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: 0.0541
- Validation Loss: 0.7193
- Train F1: 0.0838
- Epoch: 12

## 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    |


### Framework versions

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