This model identifies Name Entities : PERSON, ORGANISATION, JOB TITLE Another Model is being developped to predict relationships between these entities (nomination, départure)

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: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1
1.9205	1.0	6	1.7426	0.0
1.6476	2.0	12	1.5415	0.0
1.4607	3.0	18	1.3944	0.0635
1.3299	4.0	24	1.2587	0.4848
1.1973	5.0	30	1.1287	0.6207
1.0707	6.0	36	1.0110	0.8043
0.972	7.0	42	0.9266	0.8696
0.8877	8.0	48	0.8632	0.8602
0.8231	9.0	54	0.8279	0.8511
0.7723	10.0	60	0.8001	0.8511
0.7309	11.0	66	0.7617	0.8602
0.6902	12.0	72	0.7364	0.8602
0.6601	13.0	78	0.7104	0.8723
0.6306	14.0	84	0.7062	0.8723
0.6127	15.0	90	0.6896	0.8602
0.605	16.0	96	0.6743	0.8723
0.5892	17.0	102	0.6801	0.8723
0.5843	18.0	108	0.6797	0.8723
0.5731	19.0	114	0.6731	0.8723
0.5707	20.0	120	0.6701	0.8723

Framework versions

Transformers 4.24.0
Pytorch 1.13.0+cu117
Datasets 2.7.0
Tokenizers 0.13.2

evangeliazve/camembert-ner-articles-nomination

camembert-base-articles-ner-backup

Model description