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livinNector/
distilbert-base-multilingual-NER-naamapdam-fine-tuned

Name: distilbert-base-multilingual-NER-naamapdam-fine-tuned
Availability: InStock

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

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distilbert-base-multilingual-NER-naamapdam-fine-tuned

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

Loss: 0.3852
Precision: 0.7940
Recall: 0.8182
F1: 0.8059
Accuracy: 0.9124

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: 5e-05
train_batch_size: 256
eval_batch_size: 512
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3757	0.51	1000	0.3173	0.7666	0.7858	0.7761	0.8984
0.3062	1.03	2000	0.3020	0.7791	0.7981	0.7885	0.9026
0.2793	1.54	3000	0.2962	0.7827	0.8021	0.7923	0.9059
0.2755	2.06	4000	0.2973	0.7768	0.8122	0.7941	0.9048
0.2529	2.57	5000	0.2879	0.7747	0.8201	0.7968	0.9057
0.2483	3.08	6000	0.3025	0.7714	0.8298	0.7996	0.9079
0.2294	3.6	7000	0.2899	0.7877	0.8211	0.8041	0.9105
0.2252	4.11	8000	0.2952	0.7850	0.8185	0.8014	0.9090
0.2088	4.63	9000	0.2932	0.7851	0.8234	0.8038	0.9090
0.2046	5.14	10000	0.2998	0.7931	0.8215	0.8071	0.9117
0.1909	5.66	11000	0.3029	0.7925	0.8240	0.8080	0.9112
0.1857	6.17	12000	0.3160	0.7903	0.8228	0.8062	0.9108
0.1744	6.68	13000	0.3099	0.7858	0.8259	0.8054	0.9115
0.1686	7.2	14000	0.3199	0.7859	0.8246	0.8048	0.9097
0.1613	7.71	15000	0.3161	0.7941	0.8179	0.8058	0.9121
0.1538	8.23	16000	0.3294	0.7903	0.8221	0.8059	0.9110
0.1475	8.74	17000	0.3260	0.7935	0.8248	0.8089	0.9129
0.1429	9.25	18000	0.3378	0.7958	0.8210	0.8082	0.9130
0.1369	9.77	19000	0.3402	0.7905	0.8240	0.8069	0.9118
0.1302	10.28	20000	0.3573	0.7865	0.8269	0.8062	0.9114
0.1276	10.8	21000	0.3564	0.7924	0.8208	0.8063	0.9117
0.122	11.31	22000	0.3590	0.7939	0.8274	0.8103	0.9130
0.1181	11.83	23000	0.3660	0.7974	0.8234	0.8102	0.9132
0.1141	12.34	24000	0.3695	0.7921	0.8208	0.8062	0.9112
0.1118	12.85	25000	0.3649	0.7942	0.8188	0.8063	0.9114
0.1081	13.37	26000	0.3781	0.7980	0.8149	0.8064	0.9124
0.1054	13.88	27000	0.3800	0.7913	0.8179	0.8044	0.9120
0.1023	14.4	28000	0.3857	0.7942	0.8207	0.8072	0.9128
0.101	14.91	29000	0.3852	0.7940	0.8182	0.8059	0.9124

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3

livinNector/distilbert-base-multilingual-NER-naamapdam-fine-tuned

distilbert-base-multilingual-NER-naamapdam-fine-tuned

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

livinNector/
distilbert-base-multilingual-NER-naamapdam-fine-tuned