---
library_name: transformers
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: camembert-base-EVA
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# camembert-base-EVA

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
- Precision: 1.0
- Recall: 1.0
- F1: 1.0

## 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:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8389        | 1.0   | 875   | 0.1286          | 0.9862   | 0.9868    | 0.9862 | 0.9862 |
| 0.069         | 2.0   | 1750  | 0.0334          | 0.9954   | 0.9956    | 0.9954 | 0.9954 |
| 0.019         | 3.0   | 2625  | 0.0248          | 0.9954   | 0.9955    | 0.9954 | 0.9954 |
| 0.0103        | 4.0   | 3500  | 0.0106          | 0.9985   | 0.9985    | 0.9985 | 0.9985 |
| 0.003         | 5.0   | 4375  | 0.0357          | 0.9939   | 0.9940    | 0.9939 | 0.9939 |
| 0.0059        | 6.0   | 5250  | 0.0285          | 0.9954   | 0.9956    | 0.9954 | 0.9954 |
| 0.0087        | 7.0   | 6125  | 0.0237          | 0.9969   | 0.9970    | 0.9969 | 0.9969 |
| 0.0058        | 8.0   | 7000  | 0.0180          | 0.9969   | 0.9970    | 0.9969 | 0.9969 |
| 0.008         | 9.0   | 7875  | 0.0010          | 1.0      | 1.0       | 1.0    | 1.0    |
| 0.0002        | 10.0  | 8750  | 0.0064          | 0.9985   | 0.9985    | 0.9985 | 0.9985 |
| 0.0001        | 11.0  | 9625  | 0.0038          | 0.9985   | 0.9985    | 0.9985 | 0.9985 |
| 0.0094        | 12.0  | 10500 | 0.0327          | 0.9969   | 0.9970    | 0.9969 | 0.9969 |
| 0.0001        | 13.0  | 11375 | 0.0324          | 0.9954   | 0.9955    | 0.9954 | 0.9954 |
| 0.0028        | 14.0  | 12250 | 0.0340          | 0.9969   | 0.9970    | 0.9969 | 0.9969 |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.9.1
- Datasets 4.4.1
- Tokenizers 0.22.1