learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 128
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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy	Confusion Matrix	High Precision	High Recall	High F1
No log	1.0	5	1.4081	0.1518	0.2865	0 0 20
5 0 102
0 0 51	0.0	0.0	0.0	0.0	0.0	0.0	0.2948	1.0	0.4554
1.2754	2.0	10	1.0008	0.2503	0.6011	0 20 0
0 107 0
0 51 0	0.0	0.0	0.0	0.6011	1.0	0.7509	0.0	0.0	0.0
1.2754	3.0	15	0.9946	0.3367	0.4775	0 2 18
0 37 70
0 3 48	0.0	0.0	0.0	0.8810	0.3458	0.4966	0.3529	0.9412	0.5134
0.8128	4.0	20	0.7867	0.4046	0.6404	0 11 9
0 93 14
0 30 21	0.0	0.0	0.0	0.6940	0.8692	0.7718	0.4773	0.4118	0.4421
0.8128	5.0	25	0.7778	0.4324	0.6348	0 4 16
2 80 25
1 17 33	0.0	0.0	0.0	0.7921	0.7477	0.7692	0.4459	0.6471	0.528
0.5766	6.0	30	0.9369	0.4169	0.6292	0 5 15
2 85 20
1 23 27	0.0	0.0	0.0	0.7522	0.7944	0.7727	0.4355	0.5294	0.4779
0.5766	7.0	35	0.8983	0.4443	0.6180	1 4 15
1 78 28
2 18 31	0.25	0.05	0.0833	0.78	0.7290	0.7536	0.4189	0.6078	0.496
0.1777	8.0	40	1.5184	0.4882	0.6573	3 10 7
1 94 12
1 30 20	0.6	0.15	0.24	0.7015	0.8785	0.7801	0.5128	0.3922	0.4444
0.1777	9.0	45	1.7748	0.4364	0.5955	2 7 11
2 80 25
7 20 24	0.1818	0.1	0.1290	0.7477	0.7477	0.7477	0.4	0.4706	0.4324
0.013	10.0	50	2.1900	0.4190	0.6236	0 7 13
3 83 21
2 21 28	0.0	0.0	0.0	0.7477	0.7757	0.7615	0.4516	0.5490	0.4956
0.013	11.0	55	2.6390	0.4348	0.6404	0 6 14
1 81 25
1 17 33	0.0	0.0	0.0	0.7788	0.7570	0.7678	0.4583	0.6471	0.5366
0.0041	12.0	60	2.2662	0.4481	0.5955	4 9 7
6 84 17
8 25 18	0.2222	0.2	0.2105	0.7119	0.7850	0.7467	0.4286	0.3529	0.3871
0.0041	13.0	65	3.0654	0.4064	0.5787	0 4 16
0 64 43
3 9 39	0.0	0.0	0.0	0.8312	0.5981	0.6957	0.3980	0.7647	0.5235
0.0058	14.0	70	2.4618	0.4273	0.5899	3 9 8
2 86 19
5 30 16	0.3	0.15	0.2	0.688	0.8037	0.7414	0.3721	0.3137	0.3404
0.0058	15.0	75	2.7654	0.4147	0.5506	1 3 16
5 61 41
8 7 36	0.0714	0.05	0.0588	0.8592	0.5701	0.6854	0.3871	0.7059	0.5
0.0011	16.0	80	3.1337	0.3696	0.6180	0 15 5
1 96 10
2 35 14	0.0	0.0	0.0	0.6575	0.8972	0.7589	0.4828	0.2745	0.35
0.0011	17.0	85	2.7265	0.4388	0.5618	4 3 13
5 74 28
13 16 22	0.1818	0.2	0.1905	0.7957	0.6916	0.74	0.3492	0.4314	0.3860
0.0007	18.0	90	2.9583	0.4270	0.5843	1 3 16
1 71 35
6 13 32	0.125	0.05	0.0714	0.8161	0.6636	0.7320	0.3855	0.6275	0.4776

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.0

davanstrien/fineweb-fra_latn-quality-transformer

fineweb-fra_latn-quality-transformer

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

davanstrien/
fineweb-fra_latn-quality-transformer