This model is a fine-tuned version of almanach/camembert-base on the None dataset. It achieves the following results on the evaluation set of train/test:

Loss: 0.0128
Precision: 0.0
Recall: 0.0
F1: 0.0
Accuracy: 0.9983

The model got an accuracy of `0.998` on the seperate eval dataset : `test_fr`

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	160	0.0455	0.9975
No log	2.0	320	0.0292	0.9981
No log	3.0	480	0.0224	0.9981
0.0837	4.0	640	0.0188	0.9981
0.0837	5.0	800	0.0166	0.9979
0.0837	6.0	960	0.0148	0.9981
0.0182	7.0	1120	0.0139	0.9982
0.0182	8.0	1280	0.0133	0.9981
0.0182	9.0	1440	0.0129	0.9982
0.0122	10.0	1600	0.0128	0.9983

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

nafraa/td2

td2

Ahmed Ennaifer & Sarra Chabane Chaouche

The model got an accuracy of 0.998 on the seperate eval dataset : test_fr

Training hyperparameters

Training results

Framework versions

nafraa/
td2

The model got an accuracy of `0.998` on the seperate eval dataset : `test_fr`