distilbert-ner-cv-fine-tuned

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: 0.1041
• Precision: 0.9435
• Recall: 0.9401
• F1: 0.9418
• Accuracy: 0.9435
• Macro F1: 0.9246
• Weighted F1: 0.9417

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: 4e-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: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Macro F1	Weighted F1
0.1065	1.0	307	0.0941	0.9288	0.9296	0.9292	0.9288	0.9103	0.9292
0.08	2.0	614	0.0898	0.9347	0.9228	0.9287	0.9347	0.9038	0.9286
0.0625	3.0	921	0.0849	0.9375	0.9392	0.9383	0.9375	0.9203	0.9384
0.0533	4.0	1228	0.0926	0.9384	0.9328	0.9356	0.9384	0.9189	0.9353
0.0439	5.0	1535	0.0941	0.9414	0.9405	0.9410	0.9414	0.9251	0.9408
0.0418	6.0	1842	0.0931	0.9365	0.9365	0.9365	0.9365	0.9164	0.9365
0.0326	7.0	2149	0.1041	0.9435	0.9401	0.9418	0.9435	0.9246	0.9417
0.0311	8.0	2456	0.1025	0.9443	0.9387	0.9415	0.9443	0.9257	0.9414

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1