| | --- |
| | 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 |
| | |
