ner-model-camembert

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

• Loss: 0.1642
• Precision: 0.8721
• Recall: 0.7732
• F1: 0.8197
• Accuracy: 0.9571

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: 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: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	24	0.3640	0.0	0.0	0.0	0.8739
No log	2.0	48	0.2640	0.6884	0.4312	0.5303	0.9037
No log	3.0	72	0.2248	0.6976	0.6431	0.6692	0.9198
No log	4.0	96	0.2163	0.8182	0.6022	0.6938	0.9330
No log	5.0	120	0.1690	0.7336	0.8086	0.7692	0.9388
No log	6.0	144	0.1768	0.8558	0.6840	0.7603	0.9456
No log	7.0	168	0.1838	0.8578	0.6952	0.7680	0.9470
No log	8.0	192	0.1591	0.8158	0.8067	0.8112	0.9526
No log	9.0	216	0.1688	0.8571	0.7584	0.8047	0.9536
No log	10.0	240	0.1596	0.8431	0.7993	0.8206	0.9559
No log	11.0	264	0.1599	0.8563	0.7751	0.8137	0.9552
No log	12.0	288	0.1713	0.8515	0.7565	0.8012	0.9526
No log	13.0	312	0.1646	0.8394	0.7770	0.8069	0.9531
No log	14.0	336	0.1705	0.8367	0.7807	0.8077	0.9531
No log	15.0	360	0.1717	0.8236	0.7900	0.8065	0.9522
No log	16.0	384	0.1689	0.8631	0.7732	0.8157	0.9559
No log	17.0	408	0.1608	0.8835	0.7751	0.8257	0.9587
No log	18.0	432	0.1499	0.8849	0.7862	0.8327	0.9602
No log	19.0	456	0.1614	0.8846	0.7695	0.8231	0.9583
No log	20.0	480	0.1688	0.8448	0.7788	0.8104	0.9541
0.0983	21.0	504	0.1672	0.8482	0.7788	0.8120	0.9545
0.0983	22.0	528	0.1668	0.8563	0.7751	0.8137	0.9552
0.0983	23.0	552	0.1678	0.8545	0.7751	0.8129	0.9550
0.0983	24.0	576	0.1645	0.8703	0.7732	0.8189	0.9569
0.0983	25.0	600	0.1642	0.8721	0.7732	0.8197	0.9571

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0