This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1780
Precision: 0.9072
Recall: 0.9032
F1: 0.9052
Accuracy: 0.9072
Macro F1: 0.8952
Weighted F1: 0.9051

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: 0.0001
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: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Macro F1	Weighted F1
0.1121	1.0	522	0.1021	0.8949	0.8883	0.8916	0.8949	0.8765	0.8917
0.0998	2.0	1044	0.0949	0.8919	0.8996	0.8957	0.8919	0.8901	0.8961
0.0756	3.0	1566	0.0929	0.9044	0.9044	0.9044	0.9044	0.8949	0.9041
0.0644	4.0	2088	0.0962	0.8940	0.8903	0.8921	0.8940	0.8890	0.8922
0.0485	5.0	2610	0.1148	0.8950	0.8961	0.8956	0.8950	0.8766	0.8956
0.0408	6.0	3132	0.1177	0.8968	0.9030	0.8999	0.8968	0.8849	0.9001
0.0328	7.0	3654	0.1272	0.9007	0.8998	0.9002	0.9007	0.8862	0.9004
0.0232	8.0	4176	0.1398	0.8978	0.9106	0.9041	0.8978	0.8928	0.9045
0.015	9.0	4698	0.1547	0.9005	0.8978	0.8992	0.9005	0.8850	0.8992
0.0135	10.0	5220	0.1704	0.9030	0.9027	0.9028	0.9030	0.8901	0.9028
0.013	11.0	5742	0.1734	0.9057	0.9035	0.9046	0.9057	0.8888	0.9043
0.0108	12.0	6264	0.1780	0.9072	0.9032	0.9052	0.9072	0.8952	0.9051

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

joaopdss/distilbert-ner-cv-fine-tuned-v3

distilbert-ner-cv-fine-tuned-v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

joaopdss/
distilbert-ner-cv-fine-tuned-v3