camembert-base-EVA

This model is a fine-tuned version of 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