---
library_name: transformers
license: mit
base_model: camembert-base
tags:
- generated_from_keras_callback
model-index:
- name: RubenBueno/camembert-base-finetuned-text-classification
  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. -->

# RubenBueno/camembert-base-finetuned-text-classification

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3200
- Validation Loss: 0.2337
- Train Weighted acc: tf.Tensor(0.98794097, shape=(), dtype=float32)
- Train Accuracy: 0.9780
- Train F1: 0.9788
- Epoch: 14

## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 2040, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Weighted acc                             | Train Accuracy | Train F1 | Epoch |
|:----------:|:---------------:|:----------------------------------------------:|:--------------:|:--------:|:-----:|
| 4.3328     | 3.7022          | tf.Tensor(0.5895147, shape=(), dtype=float32)  | 0.8956         | 0.8965   | 0     |
| 3.5969     | 3.4171          | tf.Tensor(0.6352869, shape=(), dtype=float32)  | 0.8010         | 0.8365   | 1     |
| 3.2404     | 2.8696          | tf.Tensor(0.72062576, shape=(), dtype=float32) | 0.9114         | 0.9123   | 2     |
| 2.7348     | 2.1050          | tf.Tensor(0.8133777, shape=(), dtype=float32)  | 0.9215         | 0.9270   | 3     |
| 2.2544     | 1.6001          | tf.Tensor(0.8648611, shape=(), dtype=float32)  | 0.9221         | 0.9299   | 4     |
| 1.8183     | 1.2205          | tf.Tensor(0.9124412, shape=(), dtype=float32)  | 0.9334         | 0.9394   | 5     |
| 1.4753     | 0.9544          | tf.Tensor(0.9317861, shape=(), dtype=float32)  | 0.9518         | 0.9554   | 6     |
| 1.0343     | 0.7026          | tf.Tensor(0.95245564, shape=(), dtype=float32) | 0.9596         | 0.9620   | 7     |
| 0.8071     | 0.5026          | tf.Tensor(0.9712267, shape=(), dtype=float32)  | 0.9688         | 0.9702   | 8     |
| 0.6989     | 0.4500          | tf.Tensor(0.9746262, shape=(), dtype=float32)  | 0.9697         | 0.9711   | 9     |
| 0.5615     | 0.3510          | tf.Tensor(0.9816495, shape=(), dtype=float32)  | 0.9708         | 0.9722   | 10    |
| 0.4585     | 0.3062          | tf.Tensor(0.9840923, shape=(), dtype=float32)  | 0.9748         | 0.9757   | 11    |
| 0.3982     | 0.2611          | tf.Tensor(0.98653185, shape=(), dtype=float32) | 0.9753         | 0.9762   | 12    |
| 0.3399     | 0.2452          | tf.Tensor(0.98775315, shape=(), dtype=float32) | 0.9783         | 0.9790   | 13    |
| 0.3200     | 0.2337          | tf.Tensor(0.98794097, shape=(), dtype=float32) | 0.9780         | 0.9788   | 14    |


### Framework versions

- Transformers 4.50.3
- TensorFlow 2.19.0
- Datasets 3.6.0
- Tokenizers 0.21.1